A two-wheeled self-balancing robot (TWSBR) is an underactuated system that
is inherently nonlinear and unstable. While many control methods have been
introduced to enhance the performance, there is no unique solution when it comes to hardware implementation as the robot’s stability is highly dependent on accuracy of sensors and robustness of the electronic control systems. In
this study, a TWSBR that is controlled by an embedded NI myRIO-1900 board with LabVIEW-based control scheme is developed. We compare the performance between proportional-integral-derivative (PID) and linear quadratic regulator (LQR) schemes which are designed based on the TWSBR’s model that
is constructed from Newtonian principles. A hybrid PID-LQR scheme is then proposed to compensate for the individual components’ limitations. Experimental results demonstrate the PID is more effective at regulating the tilt angle of the robot in the presence of external disturbances, but it necessitates a higher velocity to sustain its equilibrium. The LQR on the other hand outperforms PID
in terms of maximum initial tilt angle. By combining both schemes, significant
improvements can be observed, such as an increase in maximum initial tilt angle
and a reduction in settling time.
Velocity control of a two-wheeled inverted pendulum mobile robot: a fuzzy mod...journalBEEI
This paper presents the design of a fuzzy tracking controller for balancing and velocity control of a Two-Wheeled Inverted Pendulum (TWIP) mobile robot based on its Takagi-Sugino (T-S) fuzzy model, fuzzy Lyapunov function and non-parallel distributed compensation (non-PDC) control law. The T-S fuzzy model of the TWIP mobile robot was developed from its nonlinear dynamical equations of motion. Stabilization conditions in a form of linear matrix inequalities (LMIs) were derived based on the T-S fuzzy model of the TWIP mobile robot, a fuzzy Lyapunov function and a non-PDC control law. Based on the derived stabilization conditions and the T-S fuzzy model of the TWIP mobile robot, a state feedback velocity tracking controller was then proposed for the TWIP mobile robot. The balancing and velocity tracking performance of the proposed controller was investigated via simulations. The simulation result shows the effectiveness of the proposed control scheme.
Balancing a Segway robot using LQR controller based on genetic and bacteria f...TELKOMNIKA JOURNAL
A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency.
IRJET- Control of Induction Motor using Neural NetworkIRJET Journal
This document describes research into using neural networks to control induction motors. It begins by introducing the topic and noting limitations of traditional PI controllers for induction motor control. It then provides details on the experimental setup, which uses an artificial neural network (ANN) to mimic a PI controller for speed control of an induction motor drive system. Simulation results are presented comparing the performance of the ANN controller to a traditional PI controller under different dynamic operating conditions. The document concludes that the ANN mapping controller provides superior performance to the PI controller.
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
This document compares different fuzzy logic controllers for improving the dynamic response of an indirect vector controlled induction motor drive. It presents a new fuzzy PI controller with scaling factors and evaluates its performance against fuzzy PI and fuzzy MRAC (model reference adaptive control) controllers. Simulation results show that the proposed fuzzy PI with scaling factors has a faster settling time than fuzzy PI, and is less complex than fuzzy MRAC while still providing good parameter insensitivity. The proposed controller provides a compromise between complexity, accuracy and settling time for induction motor applications.
This summary provides the key details from the document in 3 sentences:
The document presents a study on implementing an anti-windup PI controller for speed control of an induction machine using direct torque control (DTC) strategy. Simulation and experimental results showed that the proposed anti-windup PI controller improved the dynamic step response of speed control in terms of overshoot compared to a conventional PI controller. The validity of the control strategy was verified by implementing the anti-windup PI controller on a dSPACE 1104 board to control the speed of a 1.5 kW induction machine.
Electro-Mechanical Actuator (EMA) is the key
component in the guidance systems of missiles to convert
electrical power into mechanical power. EMAs have shown
significant improvement in response times and are more reliable
compared to other actuators. This paper proposes a Simulink
model for a linear electromechanical actuator which is very efficient
and can withstand noise and disturbances. Electromechanical
actuators are mechanical actuators where the control handle has
been supplanted by an electric motor. This model is subjected to
sudden loads and disturbances and the precise actuation is
obtained within the specified settling time. The model is also
subjected to nonlinearities and the results were found out to be
competent.
Design and Simulation study of Electro-Mechanical Actuator for Missile Maneuv...SumanthKukutam
Electro-Mechanical Actuator (EMA) is the key component in the guidance systems of missiles to convert electrical power into mechanical power. EMAs have shown significant improvement in response times and are more reliable compared to other actuators. This paper proposes a Simulink model for linear electromechanical actuator which is very efficient and can withstand noise and disturbances. Electromechanical
actuators are mechanical actuators where the control handle has been supplanted by an electric motor. This model is subjected to sudden loads and disturbances and the precise actuation is obtained within the specified settling time. The model is also subjected to nonlinearities and the results were found out to be competent.
Modeling and Control of a Spherical Rolling Robot Using Model Reference Adapt...IRJET Journal
The document describes modeling and control of a spherical rolling robot using model reference adaptive control (MRAC). It presents the mathematical model developed for the robot based on its nonholonomic constraints. MRAC is used to control the robot by adjusting controller parameters to minimize the error between the robot's actual output and the output of a reference model for ideal behavior. Simulation results show the proposed MRAC scheme eliminates steady state error and improves transient response without requiring knowledge of the robot's dynamic equations.
Velocity control of a two-wheeled inverted pendulum mobile robot: a fuzzy mod...journalBEEI
This paper presents the design of a fuzzy tracking controller for balancing and velocity control of a Two-Wheeled Inverted Pendulum (TWIP) mobile robot based on its Takagi-Sugino (T-S) fuzzy model, fuzzy Lyapunov function and non-parallel distributed compensation (non-PDC) control law. The T-S fuzzy model of the TWIP mobile robot was developed from its nonlinear dynamical equations of motion. Stabilization conditions in a form of linear matrix inequalities (LMIs) were derived based on the T-S fuzzy model of the TWIP mobile robot, a fuzzy Lyapunov function and a non-PDC control law. Based on the derived stabilization conditions and the T-S fuzzy model of the TWIP mobile robot, a state feedback velocity tracking controller was then proposed for the TWIP mobile robot. The balancing and velocity tracking performance of the proposed controller was investigated via simulations. The simulation result shows the effectiveness of the proposed control scheme.
Balancing a Segway robot using LQR controller based on genetic and bacteria f...TELKOMNIKA JOURNAL
A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency.
IRJET- Control of Induction Motor using Neural NetworkIRJET Journal
This document describes research into using neural networks to control induction motors. It begins by introducing the topic and noting limitations of traditional PI controllers for induction motor control. It then provides details on the experimental setup, which uses an artificial neural network (ANN) to mimic a PI controller for speed control of an induction motor drive system. Simulation results are presented comparing the performance of the ANN controller to a traditional PI controller under different dynamic operating conditions. The document concludes that the ANN mapping controller provides superior performance to the PI controller.
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
This document compares different fuzzy logic controllers for improving the dynamic response of an indirect vector controlled induction motor drive. It presents a new fuzzy PI controller with scaling factors and evaluates its performance against fuzzy PI and fuzzy MRAC (model reference adaptive control) controllers. Simulation results show that the proposed fuzzy PI with scaling factors has a faster settling time than fuzzy PI, and is less complex than fuzzy MRAC while still providing good parameter insensitivity. The proposed controller provides a compromise between complexity, accuracy and settling time for induction motor applications.
This summary provides the key details from the document in 3 sentences:
The document presents a study on implementing an anti-windup PI controller for speed control of an induction machine using direct torque control (DTC) strategy. Simulation and experimental results showed that the proposed anti-windup PI controller improved the dynamic step response of speed control in terms of overshoot compared to a conventional PI controller. The validity of the control strategy was verified by implementing the anti-windup PI controller on a dSPACE 1104 board to control the speed of a 1.5 kW induction machine.
Electro-Mechanical Actuator (EMA) is the key
component in the guidance systems of missiles to convert
electrical power into mechanical power. EMAs have shown
significant improvement in response times and are more reliable
compared to other actuators. This paper proposes a Simulink
model for a linear electromechanical actuator which is very efficient
and can withstand noise and disturbances. Electromechanical
actuators are mechanical actuators where the control handle has
been supplanted by an electric motor. This model is subjected to
sudden loads and disturbances and the precise actuation is
obtained within the specified settling time. The model is also
subjected to nonlinearities and the results were found out to be
competent.
Design and Simulation study of Electro-Mechanical Actuator for Missile Maneuv...SumanthKukutam
Electro-Mechanical Actuator (EMA) is the key component in the guidance systems of missiles to convert electrical power into mechanical power. EMAs have shown significant improvement in response times and are more reliable compared to other actuators. This paper proposes a Simulink model for linear electromechanical actuator which is very efficient and can withstand noise and disturbances. Electromechanical
actuators are mechanical actuators where the control handle has been supplanted by an electric motor. This model is subjected to sudden loads and disturbances and the precise actuation is obtained within the specified settling time. The model is also subjected to nonlinearities and the results were found out to be competent.
Modeling and Control of a Spherical Rolling Robot Using Model Reference Adapt...IRJET Journal
The document describes modeling and control of a spherical rolling robot using model reference adaptive control (MRAC). It presents the mathematical model developed for the robot based on its nonholonomic constraints. MRAC is used to control the robot by adjusting controller parameters to minimize the error between the robot's actual output and the output of a reference model for ideal behavior. Simulation results show the proposed MRAC scheme eliminates steady state error and improves transient response without requiring knowledge of the robot's dynamic equations.
This paper reports on the design and implementation in DSP as hardware in the loop of a nonlinear control strategy for a grid-connected variable speed wind turbine using a doubly fed induction generator (DFIG). The objective of this work is to build a real-time nonlinear hybrid approach combining Backstepping control and sliding mode control strategies for DFIG used in wind energy conversion systems (WECS). The results of the DSP implementation are discussed and qualitative and quantitative performance evaluations are performed under various disturbed conditions. The implementation is performed using the TMS320F28335 DSP combined with the MATLAB/Simulink (2016a) environment. The experimental results have been satisfactorily achieved, which implies that the proposed strategy is an efficient and robust approach to monitor the WECS.
Path tracking control of differential drive mobile robot based on chaotic-bi...IJECEIAES
This summary provides the key details about the document in 3 sentences:
The document presents a new chaotic-billiards optimizer (C-BO) algorithm to optimize the parameters of a controller for a differential-drive mobile robot. The C-BO algorithm is used to tune the controller parameters to improve path tracking performance. Simulation results show that the C-BO algorithm achieves better path tracking accuracy compared to an ant colony optimization algorithm, with a steady state error of 0.6% versus 0.8%.
Iaetsd estimation of frequency for a single link-flexibleIaetsd Iaetsd
This document proposes an adaptive control method for an uncertain flexible robotic arm. It uses a fast online closed-loop identification method combined with an output feedback controller called a Generalized Proportional Integral (GPI) controller. An algebraic identification method is used to identify unknown system parameters and update the GPI controller in real-time. Simulations show the robustness of the adaptive controller. The document describes the flexible manipulator model, GPI controller design, algebraic estimator development, adaptive control procedure, and simulation results showing the effectiveness of the adaptive control system.
1. The document presents an intelligent speed control system for an induction motor drive based on fuzzy logic. It replaces the conventional PI controller in the outer speed loop of an indirect vector control system.
2. The fuzzy logic controller uses speed error and change in speed error as inputs and outputs a change in command current. It is composed of fuzzification, fuzzy rules, inference engine, and defuzzification.
3. Simulation results using MATLAB/Simulink show that the proposed fuzzy logic controller provides better performance than a conventional PI controller for different operating conditions like load changes and reference speed changes.
IRJET- A Performance of Hybrid Control in Nonlinear Dynamic Multirotor UAVIRJET Journal
This document summarizes a research paper that models and evaluates control techniques for multirotor unmanned aerial vehicles (UAVs). It begins with an abstract that outlines the paper's contributions: developing an accurate mathematical model of a multirotor UAV using Newton-Euler dynamics, developing nonlinear control algorithms based on this model, and comparing the performance of backstepping control, sliding mode control, and fuzzy logic control through simulation. The document then provides details of the multirotor dynamic model and each control approach, evaluating their ability to stabilize the system and reject disturbances. It concludes that hybrid control systems combining advantages of different methods should be considered.
IRJET- Sensrless Luenberger Observer Based Sliding Mode Control of DC MotorIRJET Journal
This document summarizes a research paper on sensorless sliding mode control of a DC motor using a Luenberger observer. It presents a Luenberger-sliding mode observer that is simple and robust for estimating states like rotor velocity without position sensors. Simulation results show that the proposed sensorless control scheme using an adaptive sliding mode speed controller and Luenberger-sliding mode observer provides fast and accurate speed tracking over a wide range of torques for the DC motor. The observer estimates states robustly despite parameter variations or disturbances, with good tracking performance demonstrated in simulations.
Sensrless Luenberger Observer Based Sliding Mode Control of DC MotorIRJET Journal
This document describes a sensorless sliding mode control system for controlling the speed of a DC motor. It proposes using a Luenberger-sliding mode observer to estimate the motor's state variables, including rotor velocity and load torque, without requiring sensors. Simulation results show that the observer-based sliding mode controller provides fast response and accurate speed tracking over a wide range of torques. The observer design combines features of a Luenberger observer and sliding mode control to achieve robust state estimation capabilities with reduced computational complexity compared to previous observer designs.
This paper focuses on the development of a prototype of thruster motor speed controller which exhibits robust performance for an Autonomous underwater vehicle. H infinity based speed controller with particle swarm optimized weights for a sensorless BLDC motor which is used as electrical thruster has been simulated in MATLAB/ SIMULINK and implemented using TI C2000 Delfino LaunchPad LaunchXL-F28377S and BoostXL DRV 8301. A performance comparison in reference tracking has been done with conventional PI controller and experimental results have been discussed in detail. The percentage variation in speed with respect to reference speed of proposed strategy has been observed to be 0.65% whereas it is 1.1% with PI controller. It has also been found that the proposed control strategy exhibits smooth starting with better reference tracking and less torque ripples.
Comparitive Analysis of Speed and Position Control of BLDC Motor via Field Or...IRJET Journal
This document presents a comparative analysis of speed and position control of a brushless DC motor using field oriented control with two different pulse width modulation schemes: sinusoidal PWM (SPWM) and space vector PWM (SVPWM). Field oriented control is commonly used for high-performance motor control. The document models the BLDC motor and describes field oriented control, which represents stator currents in a rotating dq reference frame to separately control flux and torque. SPWM and SVPWM are two commonly used PWM techniques for motor control inverters. The analysis is carried out in Simulink to compare the performance of SPWM and SVPWM based field oriented control under different loading conditions. Preliminary results show similar performance under light loads
We focus a modern methodology in this paper for adding the fuzzy logic control as well as sliding model control. This combination can enhance the MLS position control robustness and enhanced performance of it.In the start, for an application in an area to control the loops placement and position for the synchronous motor what has permanent magnetic linearity we tend to control the fuzzy sliding mode control. To resolve the chattering issues a designed controller is investigated and, in this way, steady state motion in sliding with higher accuracy is obtained. In this case, method of online tuning with the help of fuzzy logic is used in order to adjust the thickness of boundary layer and switching gains.For the suggested scheme technique, the outcomes of simulation suggest that with the classical SMC the accurate state and good dynamic performance is compared due to force chattering resistance, response by quick dynamic force and external disturbance elements and robustness against them.
This document summarizes a journal article that proposes a fuzzy logic approach for sensorless vector control of an induction motor using an efficiency optimization technique. It presents the following:
1) A dynamic model and state space model of the induction motor in a synchronous reference frame for vector control without sensors.
2) A fuzzy logic based online efficiency optimization controller that interfaces with the drive system to minimize power consumption.
3) The controller decrements the flux in steps until the measured input power is minimized. Membership functions and rules for the fuzzy controller are provided.
4) Performance of the drive is analyzed with and without the fuzzy controller using MATLAB/Simulink simulations. The fuzzy approach is found to improve efficiency
A predictive sliding mode control for quadrotor’s tracking trajectory subject...IJECEIAES
In this paper, a predictive sliding mode control (PSMC) strategy for the quadrotors tracking trajectory problem is proposed. This strategy aims to combine the advantages of sliding mode control (SMC) and non-linear model predictive control (NMPC) to improve the tracking control performance for quadrotors in terms of optimality, inputs/states constraints satisfaction, and strong robustness against disturbances. A comparative study of three popular controllers: the SMC, NMPC, and the integral backstepping control (IBC) is performed with different criteria. Accordingly, IBC and SMC show less computational time and strong robustness, while NMPC has minimum control effort. The discrete Dryden turbulence model is used as a benchmark model to represent the wind effect on the trajectory tracking accuracy. The effectiveness of the proposed method PSMC has been proven and compared with discrete-time sliding mode control (DSMC) and NMPC in several scenarios. Simulation results show that under both wind turbulence and time-variant uncertainties, the PSMC outperforms the other controllers by providing simultaneously disturbance rejection and guarantee that the control inputs are within bounded constraints.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
Iaetsd design of a robust fuzzy logic controller for a single-link flexible m...Iaetsd Iaetsd
This document describes the design of a fuzzy logic controller for a single-link flexible manipulator. A fuzzy-PID controller is used to control an uncertain flexible robotic arm and its internal motor dynamics parameters. The controller is tested against conventional integral and PID controllers in simulations. The results show the proposed fuzzy PID controller has better robustness under variations in motor dynamics compared to the other controllers.
Elevation, pitch and travel axis stabilization of 3DOF helicopter with hybrid...IJECEIAES
This research work introduces an efficient hybrid control methodology through combining the traditional proportional-integral-derivative (PID) controller and linear quadratic regulator (LQR) optimal controlher. The proposed hybrid control approach is adopted to design three degree of freedom (3DOF) stabilizing system for helicopter. The gain parameters of the classic PID controller are determined using the elements of the LQR feedback gain matrix. The dynamic behaviour of the LQR based PID controller, is modeled in state space form to enable utlizing state feedback controller technique. The performance of the proposed LQR based LQR controller is improved by using Genetic Algorithm optimization method which are adopted to obtain optimum values for LQR controller gain parameters. The LQR-PID hybrid controller is simulated using Matlab environment and its performance is evaluated based on rise time, settling time, overshoot and steady state error parameters to validate the proposed 3DOF helicopter balancing system. Based on GA tuning approach, the simulation results suggest that the hybrid LQR-PID controller can be effectively employed to stabilize the 3DOF helicopter system.
A Review on Rapid Control of a Brushless Motor in an Hybrid Systemsunil kumar
This document discusses the rapid control of a brushless motor in a hybrid system. It presents an experimental setup that uses electromagnetic clutches to allow power transfer between a brushless DC motor and an internal combustion engine via pulleys. An incremental encoder is used to measure motor angular velocity, which is fed back in a control loop to synchronize motor and engine speeds. Both classic PID control and fuzzy logic control are explored. Simulation results show that a fuzzy proportional-integral controller combined with a PID controller helps autotune gains in real-time and improves rise time and settling time compared to conventional tuning methods. The control system aims to optimize fuel efficiency in the hybrid system.
IRJET- Performance Analysis of Speed Control of Induction Motor using Pi,...IRJET Journal
This document analyzes and compares the performance of PI, sliding mode, and fuzzy logic controllers for speed control of an induction motor. It first provides background on vector control of induction motors and derives the mathematical models of the motor. It then describes the design and implementation of the three controllers - PI, sliding mode, and fuzzy logic. Simulation results show that the sliding mode controller provides the best dynamic performance and robustness to load disturbances, followed by the fuzzy logic controller, while the PI controller has less satisfactory steady state response and performance under disturbances. The document concludes by comparing the performance of the three controllers.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Induction motor harmonic reduction using space vector modulation algorithmjournalBEEI
The vector control was proposed as an alternative to the scalar control for AC machines control. Vector control provide high operation performance in steady state and transient operation. However, the variable switching frequency of vector control causes high flux and torque ripples which lead to an acoustical noise and degrade the performance of the control scheme. The insertion of the space vector modulation was a very useful solution to reduce the high ripples level inspite of its complexity. Numerical simulation results obtained in MATLAB/Simulink show the good dynamic performance of the proposed vector control technique and the effectiveness of the proposed sensorless strategy in the presence of the sudden load torque basing on the integral backstepping approach capabilities on instant perturbation rejection.
Keywords
This document summarizes a research paper that presents a digital speed control system for a permanent magnet brushless DC motor using a TMS320LF2407 digital signal processor (DSP) controller. The paper describes modeling the brushless DC motor in MATLAB/Simulink using classical modeling equations. A digital PI controller is implemented in the simulation for closed-loop speed control. The control algorithms are also implemented on the TMS320LF2407 DSP controller in hardware. The system aims to provide wide speed control for applications like electric vehicles through advanced digital control techniques.
Because of the rapid growth in technology breakthroughs, including
multimedia and cell phones, Telugu character recognition (TCR) has recently
become a popular study area. It is still necessary to construct automated and
intelligent online TCR models, even if many studies have focused on offline
TCR models. The Telugu character dataset construction and validation using
an Inception and ResNet-based model are presented. The collection of 645
letters in the dataset includes 18 Achus, 38 Hallus, 35 Othulu, 34×16
Guninthamulu, and 10 Ankelu. The proposed technique aims to efficiently
recognize and identify distinctive Telugu characters online. This model's main
pre-processing steps to achieve its goals include normalization, smoothing,
and interpolation. Improved recognition performance can be attained by using
stochastic gradient descent (SGD) to optimize the model's hyperparameters.
Scientific workload execution on a distributed computing platform such as a
cloud environment is time-consuming and expensive. The scientific workload
has task dependencies with different service level agreement (SLA)
prerequisites at different levels. Existing workload scheduling (WS) designs
are not efficient in assuring SLA at the task level. Alongside, induces higher
costs as the majority of scheduling mechanisms reduce either time or energy.
In reducing, cost both energy and makespan must be optimized together for
allocating resources. No prior work has considered optimizing energy and
processing time together in meeting task level SLA requirements. This paper
presents task level energy and performance assurance-workload scheduling
(TLEPA-WS) algorithm for the distributed computing environment. The
TLEPA-WS guarantees energy minimization with the performance
requirement of the parallel application under a distributed computational
environment. Experiment results show a significant reduction in using energy
and makespan; thereby reducing the cost of workload execution in comparison
with various standard workload execution models.
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This paper reports on the design and implementation in DSP as hardware in the loop of a nonlinear control strategy for a grid-connected variable speed wind turbine using a doubly fed induction generator (DFIG). The objective of this work is to build a real-time nonlinear hybrid approach combining Backstepping control and sliding mode control strategies for DFIG used in wind energy conversion systems (WECS). The results of the DSP implementation are discussed and qualitative and quantitative performance evaluations are performed under various disturbed conditions. The implementation is performed using the TMS320F28335 DSP combined with the MATLAB/Simulink (2016a) environment. The experimental results have been satisfactorily achieved, which implies that the proposed strategy is an efficient and robust approach to monitor the WECS.
Path tracking control of differential drive mobile robot based on chaotic-bi...IJECEIAES
This summary provides the key details about the document in 3 sentences:
The document presents a new chaotic-billiards optimizer (C-BO) algorithm to optimize the parameters of a controller for a differential-drive mobile robot. The C-BO algorithm is used to tune the controller parameters to improve path tracking performance. Simulation results show that the C-BO algorithm achieves better path tracking accuracy compared to an ant colony optimization algorithm, with a steady state error of 0.6% versus 0.8%.
Iaetsd estimation of frequency for a single link-flexibleIaetsd Iaetsd
This document proposes an adaptive control method for an uncertain flexible robotic arm. It uses a fast online closed-loop identification method combined with an output feedback controller called a Generalized Proportional Integral (GPI) controller. An algebraic identification method is used to identify unknown system parameters and update the GPI controller in real-time. Simulations show the robustness of the adaptive controller. The document describes the flexible manipulator model, GPI controller design, algebraic estimator development, adaptive control procedure, and simulation results showing the effectiveness of the adaptive control system.
1. The document presents an intelligent speed control system for an induction motor drive based on fuzzy logic. It replaces the conventional PI controller in the outer speed loop of an indirect vector control system.
2. The fuzzy logic controller uses speed error and change in speed error as inputs and outputs a change in command current. It is composed of fuzzification, fuzzy rules, inference engine, and defuzzification.
3. Simulation results using MATLAB/Simulink show that the proposed fuzzy logic controller provides better performance than a conventional PI controller for different operating conditions like load changes and reference speed changes.
IRJET- A Performance of Hybrid Control in Nonlinear Dynamic Multirotor UAVIRJET Journal
This document summarizes a research paper that models and evaluates control techniques for multirotor unmanned aerial vehicles (UAVs). It begins with an abstract that outlines the paper's contributions: developing an accurate mathematical model of a multirotor UAV using Newton-Euler dynamics, developing nonlinear control algorithms based on this model, and comparing the performance of backstepping control, sliding mode control, and fuzzy logic control through simulation. The document then provides details of the multirotor dynamic model and each control approach, evaluating their ability to stabilize the system and reject disturbances. It concludes that hybrid control systems combining advantages of different methods should be considered.
IRJET- Sensrless Luenberger Observer Based Sliding Mode Control of DC MotorIRJET Journal
This document summarizes a research paper on sensorless sliding mode control of a DC motor using a Luenberger observer. It presents a Luenberger-sliding mode observer that is simple and robust for estimating states like rotor velocity without position sensors. Simulation results show that the proposed sensorless control scheme using an adaptive sliding mode speed controller and Luenberger-sliding mode observer provides fast and accurate speed tracking over a wide range of torques for the DC motor. The observer estimates states robustly despite parameter variations or disturbances, with good tracking performance demonstrated in simulations.
Sensrless Luenberger Observer Based Sliding Mode Control of DC MotorIRJET Journal
This document describes a sensorless sliding mode control system for controlling the speed of a DC motor. It proposes using a Luenberger-sliding mode observer to estimate the motor's state variables, including rotor velocity and load torque, without requiring sensors. Simulation results show that the observer-based sliding mode controller provides fast response and accurate speed tracking over a wide range of torques. The observer design combines features of a Luenberger observer and sliding mode control to achieve robust state estimation capabilities with reduced computational complexity compared to previous observer designs.
This paper focuses on the development of a prototype of thruster motor speed controller which exhibits robust performance for an Autonomous underwater vehicle. H infinity based speed controller with particle swarm optimized weights for a sensorless BLDC motor which is used as electrical thruster has been simulated in MATLAB/ SIMULINK and implemented using TI C2000 Delfino LaunchPad LaunchXL-F28377S and BoostXL DRV 8301. A performance comparison in reference tracking has been done with conventional PI controller and experimental results have been discussed in detail. The percentage variation in speed with respect to reference speed of proposed strategy has been observed to be 0.65% whereas it is 1.1% with PI controller. It has also been found that the proposed control strategy exhibits smooth starting with better reference tracking and less torque ripples.
Comparitive Analysis of Speed and Position Control of BLDC Motor via Field Or...IRJET Journal
This document presents a comparative analysis of speed and position control of a brushless DC motor using field oriented control with two different pulse width modulation schemes: sinusoidal PWM (SPWM) and space vector PWM (SVPWM). Field oriented control is commonly used for high-performance motor control. The document models the BLDC motor and describes field oriented control, which represents stator currents in a rotating dq reference frame to separately control flux and torque. SPWM and SVPWM are two commonly used PWM techniques for motor control inverters. The analysis is carried out in Simulink to compare the performance of SPWM and SVPWM based field oriented control under different loading conditions. Preliminary results show similar performance under light loads
We focus a modern methodology in this paper for adding the fuzzy logic control as well as sliding model control. This combination can enhance the MLS position control robustness and enhanced performance of it.In the start, for an application in an area to control the loops placement and position for the synchronous motor what has permanent magnetic linearity we tend to control the fuzzy sliding mode control. To resolve the chattering issues a designed controller is investigated and, in this way, steady state motion in sliding with higher accuracy is obtained. In this case, method of online tuning with the help of fuzzy logic is used in order to adjust the thickness of boundary layer and switching gains.For the suggested scheme technique, the outcomes of simulation suggest that with the classical SMC the accurate state and good dynamic performance is compared due to force chattering resistance, response by quick dynamic force and external disturbance elements and robustness against them.
This document summarizes a journal article that proposes a fuzzy logic approach for sensorless vector control of an induction motor using an efficiency optimization technique. It presents the following:
1) A dynamic model and state space model of the induction motor in a synchronous reference frame for vector control without sensors.
2) A fuzzy logic based online efficiency optimization controller that interfaces with the drive system to minimize power consumption.
3) The controller decrements the flux in steps until the measured input power is minimized. Membership functions and rules for the fuzzy controller are provided.
4) Performance of the drive is analyzed with and without the fuzzy controller using MATLAB/Simulink simulations. The fuzzy approach is found to improve efficiency
A predictive sliding mode control for quadrotor’s tracking trajectory subject...IJECEIAES
In this paper, a predictive sliding mode control (PSMC) strategy for the quadrotors tracking trajectory problem is proposed. This strategy aims to combine the advantages of sliding mode control (SMC) and non-linear model predictive control (NMPC) to improve the tracking control performance for quadrotors in terms of optimality, inputs/states constraints satisfaction, and strong robustness against disturbances. A comparative study of three popular controllers: the SMC, NMPC, and the integral backstepping control (IBC) is performed with different criteria. Accordingly, IBC and SMC show less computational time and strong robustness, while NMPC has minimum control effort. The discrete Dryden turbulence model is used as a benchmark model to represent the wind effect on the trajectory tracking accuracy. The effectiveness of the proposed method PSMC has been proven and compared with discrete-time sliding mode control (DSMC) and NMPC in several scenarios. Simulation results show that under both wind turbulence and time-variant uncertainties, the PSMC outperforms the other controllers by providing simultaneously disturbance rejection and guarantee that the control inputs are within bounded constraints.
The aim of this research is the speed tracking of the permanent magnet synchronous motor (PMSM) using an intelligent Neural-Network based adapative backstepping control. First, the model of PMSM in the Park synchronous frame is derived. Then, the PMSM speed regulation is investigated using the classical method utilizing the field oriented control theory. Thereafter, a robust nonlinear controller employing an adaptive backstepping strategy is investigated in order to achieve a good performance tracking objective under motor parameters changing and external load torque application. In the final step, a neural network estimator is integrated with the adaptive controller to estimate the motor parameters values and the load disturbance value for enhancing the effectiveness of the adaptive backstepping controller. The robsutness of the presented control algorithm is demonstrated using simulation tests. The obtained results clearly demonstrate that the presented NN-adaptive control algorithm can provide good trackingperformances for the speed trackingin the presence of motor parameter variation and load application.
Iaetsd design of a robust fuzzy logic controller for a single-link flexible m...Iaetsd Iaetsd
This document describes the design of a fuzzy logic controller for a single-link flexible manipulator. A fuzzy-PID controller is used to control an uncertain flexible robotic arm and its internal motor dynamics parameters. The controller is tested against conventional integral and PID controllers in simulations. The results show the proposed fuzzy PID controller has better robustness under variations in motor dynamics compared to the other controllers.
Elevation, pitch and travel axis stabilization of 3DOF helicopter with hybrid...IJECEIAES
This research work introduces an efficient hybrid control methodology through combining the traditional proportional-integral-derivative (PID) controller and linear quadratic regulator (LQR) optimal controlher. The proposed hybrid control approach is adopted to design three degree of freedom (3DOF) stabilizing system for helicopter. The gain parameters of the classic PID controller are determined using the elements of the LQR feedback gain matrix. The dynamic behaviour of the LQR based PID controller, is modeled in state space form to enable utlizing state feedback controller technique. The performance of the proposed LQR based LQR controller is improved by using Genetic Algorithm optimization method which are adopted to obtain optimum values for LQR controller gain parameters. The LQR-PID hybrid controller is simulated using Matlab environment and its performance is evaluated based on rise time, settling time, overshoot and steady state error parameters to validate the proposed 3DOF helicopter balancing system. Based on GA tuning approach, the simulation results suggest that the hybrid LQR-PID controller can be effectively employed to stabilize the 3DOF helicopter system.
A Review on Rapid Control of a Brushless Motor in an Hybrid Systemsunil kumar
This document discusses the rapid control of a brushless motor in a hybrid system. It presents an experimental setup that uses electromagnetic clutches to allow power transfer between a brushless DC motor and an internal combustion engine via pulleys. An incremental encoder is used to measure motor angular velocity, which is fed back in a control loop to synchronize motor and engine speeds. Both classic PID control and fuzzy logic control are explored. Simulation results show that a fuzzy proportional-integral controller combined with a PID controller helps autotune gains in real-time and improves rise time and settling time compared to conventional tuning methods. The control system aims to optimize fuel efficiency in the hybrid system.
IRJET- Performance Analysis of Speed Control of Induction Motor using Pi,...IRJET Journal
This document analyzes and compares the performance of PI, sliding mode, and fuzzy logic controllers for speed control of an induction motor. It first provides background on vector control of induction motors and derives the mathematical models of the motor. It then describes the design and implementation of the three controllers - PI, sliding mode, and fuzzy logic. Simulation results show that the sliding mode controller provides the best dynamic performance and robustness to load disturbances, followed by the fuzzy logic controller, while the PI controller has less satisfactory steady state response and performance under disturbances. The document concludes by comparing the performance of the three controllers.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Induction motor harmonic reduction using space vector modulation algorithmjournalBEEI
The vector control was proposed as an alternative to the scalar control for AC machines control. Vector control provide high operation performance in steady state and transient operation. However, the variable switching frequency of vector control causes high flux and torque ripples which lead to an acoustical noise and degrade the performance of the control scheme. The insertion of the space vector modulation was a very useful solution to reduce the high ripples level inspite of its complexity. Numerical simulation results obtained in MATLAB/Simulink show the good dynamic performance of the proposed vector control technique and the effectiveness of the proposed sensorless strategy in the presence of the sudden load torque basing on the integral backstepping approach capabilities on instant perturbation rejection.
Keywords
This document summarizes a research paper that presents a digital speed control system for a permanent magnet brushless DC motor using a TMS320LF2407 digital signal processor (DSP) controller. The paper describes modeling the brushless DC motor in MATLAB/Simulink using classical modeling equations. A digital PI controller is implemented in the simulation for closed-loop speed control. The control algorithms are also implemented on the TMS320LF2407 DSP controller in hardware. The system aims to provide wide speed control for applications like electric vehicles through advanced digital control techniques.
Similar to Self-balancing robot: modeling and comparative analysis between PID and linear quadratic regulator (20)
Because of the rapid growth in technology breakthroughs, including
multimedia and cell phones, Telugu character recognition (TCR) has recently
become a popular study area. It is still necessary to construct automated and
intelligent online TCR models, even if many studies have focused on offline
TCR models. The Telugu character dataset construction and validation using
an Inception and ResNet-based model are presented. The collection of 645
letters in the dataset includes 18 Achus, 38 Hallus, 35 Othulu, 34×16
Guninthamulu, and 10 Ankelu. The proposed technique aims to efficiently
recognize and identify distinctive Telugu characters online. This model's main
pre-processing steps to achieve its goals include normalization, smoothing,
and interpolation. Improved recognition performance can be attained by using
stochastic gradient descent (SGD) to optimize the model's hyperparameters.
Scientific workload execution on a distributed computing platform such as a
cloud environment is time-consuming and expensive. The scientific workload
has task dependencies with different service level agreement (SLA)
prerequisites at different levels. Existing workload scheduling (WS) designs
are not efficient in assuring SLA at the task level. Alongside, induces higher
costs as the majority of scheduling mechanisms reduce either time or energy.
In reducing, cost both energy and makespan must be optimized together for
allocating resources. No prior work has considered optimizing energy and
processing time together in meeting task level SLA requirements. This paper
presents task level energy and performance assurance-workload scheduling
(TLEPA-WS) algorithm for the distributed computing environment. The
TLEPA-WS guarantees energy minimization with the performance
requirement of the parallel application under a distributed computational
environment. Experiment results show a significant reduction in using energy
and makespan; thereby reducing the cost of workload execution in comparison
with various standard workload execution models.
Investigating human subjects is the goal of predicting human emotions in the
real world scenario. A significant number of psychological effects require
(feelings) to be produced, directly releasing human emotions. The
development of effect theory leads one to believe that one must be aware of
one's sentiments and emotions to forecast one's behavior. The proposed line
of inquiry focuses on developing a reliable model incorporating
neurophysiological data into actual feelings. Any change in emotional affect
will directly elicit a response in the body's physiological systems. This
approach is named after the notion of Gaussian mixture models (GMM). The
statistical reaction following data processing, quantitative findings on emotion
labels, and coincidental responses with training samples all directly impact the
outcomes that are accomplished. In terms of statistical parameters such as
population mean and standard deviation, the suggested method is evaluated
compared to a technique considered to be state-of-the-art. The proposed
system determines an individual's emotional state after a minimum of 6
iterative learning using the Gaussian expectation-maximization (GEM)
statistical model, in which the iterations tend to continue to zero error. Perhaps
each of these improves predictions while simultaneously increasing the
amount of value extracted.
Early diagnosis of cancers is a major requirement for patients and a
complicated job for the oncologist. If it is diagnosed early, it could have made
the patient more likely to live. For a few decades, fuzzy logic emerged as an
emphatic technique in the identification of diseases like different types of
cancers. The recognition of cancer diseases mostly operated with inexactness,
inaccuracy, and vagueness. This paper aims to design the fuzzy expert system
(FES) and its implementation for the detection of prostate cancer. Specifically,
prostate-specific antigen (PSA), prostate volume (PV), age, and percentage
free PSA (%FPSA) are used to determine prostate cancer risk (PCR), while
PCR serves as an output parameter. Mamdani fuzzy inference method is used
to calculate a range of PCR. The system provides a scale of risk of prostate
cancer and clears the path for the oncologist to determine whether their
patients need a biopsy. This system is fast as it requires minimum calculation
and hence comparatively less time which reduces mortality and morbidity and
is more reliable than other economic systems and can be frequently used by
doctors.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
Class imbalance is a pervasive issue in the field of disease classification from
medical images. It is necessary to balance out the class distribution while training a model. However, in the case of rare medical diseases, images from affected
patients are much harder to come by compared to images from non-affected
patients, resulting in unwanted class imbalance. Various processes of tackling
class imbalance issues have been explored so far, each having its fair share of
drawbacks. In this research, we propose an outlier detection based image classification technique which can handle even the most extreme case of class imbalance. We have utilized a dataset of malaria parasitized and uninfected cells. An
autoencoder model titled AnoMalNet is trained with only the uninfected cell images at the beginning and then used to classify both the affected and non-affected
cell images by thresholding a loss value. We have achieved an accuracy, precision, recall, and F1 score of 98.49%, 97.07%, 100%, and 98.52% respectively,
performing better than large deep learning models and other published works.
As our proposed approach can provide competitive results without needing the
disease-positive samples during training, it should prove to be useful in binary
disease classification on imbalanced datasets.
Recently, plant identification has become an active trend due to encouraging
results achieved in plant species detection and plant classification fields
among numerous available plants using deep learning methods. Therefore,
plant classification analysis is performed in this work to address the problem
of accurate plant species detection in the presence of multiple leaves together,
flowers, and noise. Thus, a convolutional neural network based deep feature
learning and classification (CNN-DFLC) model is designed to analyze
patterns of plant leaves and perform classification using generated finegrained feature weights. The proposed CNN-DFLC model precisely estimates
which the given image belongs to which plant species. Several layers and
blocks are utilized to design the proposed CNN-DFLC model. Fine-grained
feature weights are obtained using convolutional and pooling layers. The
obtained feature maps in training are utilized to predict labels and model
performance is tested on the Vietnam plant image (VPN-200) dataset. This
dataset consists of a total number of 20,000 images and testing results are
achieved in terms of classification accuracy, precision, recall, and other
performance metrics. The mean classification accuracy obtained using the
proposed CNN-DFLC model is 96.42% considering all 200 classes from the
VPN-200 dataset.
Big data as a service (BDaaS) platform is widely used by various
organizations for handling and processing the high volume of data generated
from different internet of things (IoT) devices. Data generated from these IoT
devices are kept in the form of big data with the help of cloud computing
technology. Researchers are putting efforts into providing a more secure and
protected access environment for the data available on the cloud. In order to
create a safe, distributed, and decentralised environment in the cloud,
blockchain technology has emerged as a useful tool. In this research paper, we
have proposed a system that uses blockchain technology as a tool to regulate
data access that is provided by BDaaS platforms. We are securing the access
policy of data by using a modified form of ciphertext policy-attribute based
encryption (CP-ABE) technique with the help of blockchain technology. For
secure data access in BDaaS, algorithms have been created using a mix of CPABE with blockchain technology. Proposed smart contract algorithms are
implemented using Eclipse 7.0 IDE and the cloud environment has been
simulated on CloudSim tool. Results of key generation time, encryption time,
and decryption time has been calculated and compared with access control
mechanism without blockchain technology.
Internet of things (IoT) has become one of the eminent phenomena in human
life along with its collaboration with wireless sensor networks (WSNs), due
to enormous growth in the domain; there has been a demand to address the
various issues regarding it such as energy consumption, redundancy, and
overhead. Data aggregation (DA) is considered as the basic mechanism to
minimize the energy efficiency and communication overhead; however,
security plays an important role where node security is essential due to the
volatile nature of WSN. Thus, we design and develop proximate node aware
secure data aggregation (PNA-SDA). In the PNA-SDA mechanism, additional
data is used to secure the original data, and further information is shared with
the proximate node; moreover, further security is achieved by updating the
state each time. Moreover, the node that does not have updated information is
considered as the compromised node and discarded. PNA-SDA is evaluated
considering the different parameters like average energy consumption, and
average deceased node; also, comparative analysis is carried out with the
existing model in terms of throughput and correct packet identification.
Drones provide an alternative progression in protection submissions since
they are capable of conducting autonomous seismic investigations. Recent
advancement in unmanned aerial vehicle (UAV) communication is an internet
of a drone combined with 5G networks. Because of the quick utilization of
rapidly progressed registering frameworks besides 5G officialdoms, the
information from the user is consistently refreshed and pooled. Thus, safety
or confidentiality is vital among clients, and a proficient substantiation
methodology utilizing a vigorous sanctuary key. Conventional procedures
ensure a few restrictions however taking care of the assault arrangements in
information transmission over the internet of drones (IOD) environmental
frameworks. A unique hyperelliptical curve (HEC) cryptographically based
validation system is proposed to provide protected data facilities among
drones. The proposed method has been compared with the existing methods
in terms of packet loss rate, computational cost, and delay and thereby
provides better insight into efficient and secure communication. Finally, the
simulation results show that our strategy is efficient in both computation and
communication.
Monitoring behavior, numerous actions, or any such information is considered
as surveillance and is done for information gathering, influencing, managing,
or directing purposes. Citizens employ surveillance to safeguard their
communities. Governments do this for the purposes of intelligence collection,
including espionage, crime prevention, the defense of a method, a person, a
group, or an item; or the investigation of criminal activity. Using an internet
of things (IoT) rover, the area will be secured with better secrecy and
efficiency instead of humans, will provide an additional safety step. In this
paper, there is a discussion about an IoT rover for remote surveillance based
around a Raspberry Pi microprocessor which will be able to monitor a
closed/open space. This rover will allow safer survey operations and would
help to reduce the risks involved with it.
In a world where climate change looms large the spotlight often shines on
greenhouse gases, but the shadow of man-made aerosols should not be
underestimated. These tiny particles play a pivotal role in disrupting Earth's
radiative equilibrium, yet many mysteries surround their influence on various
physical aspects of our planet. The root of these mysteries lies in the limited
data we have on aerosol sources, formation processes, conversion dynamics,
and collection methods. Aerosols, composed of particulate matter (PM),
sulfates, and nitrates, hold significant sway across the hemisphere. Accurate
measurement demands the refinement of in-situ, satellite, and ground-based
techniques. As aerosols interact intricately with the environment, their full
impact remains an enigma. Enter a groundbreaking study in Morocco that
dared to compare an internet of thing (IoT) system with satellite-based
atmospheric models, with a focus on fine particles below 10 and 2.5
micrometers in diameter. The initial results, particularly in regions abundant
with extraction pits, shed light on the IoT system's potential to decode
aerosols' role in the grand narrative of climate change. These findings inspire
hope as we confront the formidable global challenge of climate change.
The use of technology has a significant impact to reduce the consequences of
accidents. Sensors, small components that detect interactions experienced by
various components, play a crucial role in this regard. This study focuses on
how the MPU6050 sensor module can be used to detect the movement of
people who are falling, defined as the inability of the lower body, including
the hips and feet, to support the body effectively. An airbag system is
proposed to reduce the impact of a fall. The data processing method in this
study involves the use of a threshold value to identify falling motion. The
results of the study have identified a threshold value for falling motion,
including an acceleration relative (AR) value of less than or equal to 0.38 g,
an angle slope of more than or equal to 40 degrees, and an angular velocity
of more than or equal to 30 °/s. The airbag system is designed to inflate
faster than the time of impact, with a gas flow rate of 0.04876 m3
/s and an
inflating time of 0.05 s. The overall system has a specificity value of 100%,
a sensitivity of 85%, and an accuracy of 94%.
The fundamental principle of the paper is that the soil moisture sensor obtains
the moisture content level of the soil sample. The water pump is automatically
activated if the moisture content is insufficient, which causes water to flow
into the soil. The water pump is immediately turned off when the moisture
content is high enough. Smart home, smart city, smart transportation, and
smart farming are just a few of the new intelligent ideas that internet of things
(IoT) includes. The goal of this method is to increase productivity and
decrease manual labour among farmers. In this paper, we present a system for
monitoring and regulating water flow that employs a soil moisture sensor to
keep track of soil moisture content as well as the land’s water level to keep
track of and regulate the amount of water supplied to the plant. The device
also includes an automated led lighting system.
In order to provide sensing services to low-powered IoT devices, wireless sensor networks (WSNs) organize specialized transducers into networks. Energy usage is one of the most important design concerns in WSN because it is very hard to replace or recharge the batteries in sensor nodes. For an energy-constrained network, the clustering technique is crucial in preserving battery life. By strategically selecting a cluster head (CH), a network's load can be balanced, resulting in decreased energy usage and extended system life. Although clustering has been predominantly used in the literature, the concept of chain-based clustering has not yet been explored. As a result, in this paper, we employ a chain-based clustering architecture for data dissemination in the network. Furthermore, for CH selection, we employ the coati optimisation algorithm, which was recently proposed and has demonstrated significant improvement over other optimization algorithms. In this method, the parameters considered for selecting the CH are energy, node density, distance, and the network’s average energy. The simulation results show tremendous improvement over the competitive cluster-based routing algorithms in the context of network lifetime, stability period (first node dead), transmission rate, and the network's power reserves.
The construction industry is an industry that is always surrounded by
uncertainties and risks. The industry is always associated with a threatindustry which has a complex, tedious layout and techniques characterized by
unpredictable circumstances. It comprises a variety of human talents and the
coordination of different areas and activities associated with it. In this
competitive era of the construction industry, delays and cost overruns of the
project are often common in every project and the causes of that are also
common. One of the problems which we are trying to cater to is the improper
handling of materials at the construction site. In this paper, we propose
developing a system that is capable of tracking construction material on site
that would benefit the contractor and client for better control over inventory
on-site and to minimize loss of material that occurs due to theft and misplacing
of materials.
Today, health monitoring relies heavily on technological advancements. This
study proposes a low-power wide-area network (LPWAN) based, multinodal
health monitoring system to monitor vital physiological data. The suggested
system consists of two nodes, an indoor node, and an outdoor node, and the
nodes communicate via long range (LoRa) transceivers. Outdoor nodes use an
MPU6050 module, heart rate, oxygen pulse, temperature, and skin resistance
sensors and transmit sensed values to the indoor node. We transferred the data
received by the master node to the cloud using the Adafruit cloud service. The
system can operate with a coverage of 4.5 km, where the optimal distance
between outdoor sensor nodes and the indoor master node is 4 km. To further
predict fall detection, various machine learning classification techniques have
been applied. Upon comparing various classifier techniques, the decision tree
method achieved an accuracy of 0.99864 with a training and testing ratio of
70:30. By developing accurate prediction models, we can identify high-risk
individuals and implement preventative measures to reduce the likelihood of
a fall occurring. Remote monitoring of the health and physical status of elderly
people has proven to be the most beneficial application of this technology.
The effectiveness of adaptive filters are mainly dependent on the design
techniques and the algorithm of adaptation. The most common adaptation
technique used is least mean square (LMS) due its computational simplicity.
The application depends on the adaptive filter configuration used and are well
known for system identification and real time applications. In this work, a
modified delayed μ-law proportionate normalized least mean square
(DMPNLMS) algorithm has been proposed. It is the improvised version of the
µ-law proportionate normalized least mean square (MPNLMS) algorithm.
The algorithm is realized using Ladner-Fischer type of parallel prefix
logarithmic adder to reduce the silicon area. The simulation and
implementation of very large-scale integration (VLSI) architecture are done
using MATLAB, Vivado suite and complementary metal–oxide–
semiconductor (CMOS) 90 nm technology node using Cadence RTL and
Genus Compiler respectively. The DMPNLMS method exhibits a reduction
in mean square error, a higher rate of convergence, and more stability. The
synthesis results demonstrate that it is area and delay effective, making it
practical for applications where a faster operating speed is required.
The increasing demand for faster, robust, and efficient device development of enabling technology to mass production of industrial research in circuit design deals with challenges like size, efficiency, power, and scalability. This paper, presents a design and analysis of low power high speed full adder using negative capacitance field effecting transistors. A comprehensive study is performed with adiabatic logic and reversable logic. The performance of full adder is studied with metal oxide field effect transistor (MOSFET) and negative capacitance field effecting (NCFET). The NCFET based full adder offers a low power and high speed compared with conventional MOSFET. The complete design and analysis are performed using cadence virtuoso. The adiabatic logic offering low delay of 0.023 ns and reversable logic is offering low power of 7.19 mw.
The global agriculture system faces significant challenges in meeting the
growing demand for food production, particularly given projections that the
world's population will reach 70% by 2050. Hydroponic farming is an
increasingly popular technique in this field, offering a promising solution to
these challenges. This paper will present the improvement of the current
traditional hydroponic method by providing a system that can be used to
monitor and control the important element in order to help the plant grow up
smoothly. This proposed system is quite efficient and user-friendly that can
be used by anyone. This is a combination of a traditional hydroponic system,
an automatic control system and a smartphone. The primary objective is to
develop a smart system capable of monitoring and controlling potential
hydrogen (pH) levels, a key factor that affects hydroponic plant growth.
Ultimately, this paper offers an alternative approach to address the challenges
of the existing agricultural system and promote the production of clean,
disease-free, and healthy food for a better future.
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Self-balancing robot: modeling and comparative analysis between PID and linear quadratic regulator
1. International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol. 12, No. 3, November 2023, pp. 351∼359
ISSN: 2089-4864, DOI: 10.11591/ijres.v12.i3.pp351-359 ❒ 351
Self-balancing robot: modeling and comparative analysis
between PID and linear quadratic regulator
Lu Bin Lau, Nur Syazreen Ahmad, Patrick Goh
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
Article Info
Article history:
Received Oct 20, 2022
Revised Mar 19, 2023
Accepted Apr 4, 2023
Keywords:
Linear quadratic regulator
Modeling
Proportional-integral-derivative
Robot
Self-balancing
ABSTRACT
A two-wheeled self-balancing robot (TWSBR) is an underactuated system that
is inherently nonlinear and unstable. While many control methods have been
introduced to enhance the performance, there is no unique solution when it
comes to hardware implementation as the robot’s stability is highly dependent
on accuracy of sensors and robustness of the electronic control systems. In
this study, a TWSBR that is controlled by an embedded NI myRIO-1900 board
with LabVIEW-based control scheme is developed. We compare the perfor-
mance between proportional-integral-derivative (PID) and linear quadratic reg-
ulator (LQR) schemes which are designed based on the TWSBR’s model that
is constructed from Newtonian principles. A hybrid PID-LQR scheme is then
proposed to compensate for the individual components’ limitations. Experimen-
tal results demonstrate the PID is more effective at regulating the tilt angle of
the robot in the presence of external disturbances, but it necessitates a higher
velocity to sustain its equilibrium. The LQR on the other hand outperforms PID
in terms of maximum initial tilt angle. By combining both schemes, significant
improvements can be observed, such as an increase in maximum initial tilt angle
and a reduction in settling time.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Nur Syazreen Ahmad
School of Electrical and Electronic Engineering, Universiti Sains Malaysia
14300 Nibong Tebal, Penang, Malaysia
Email: syazreen@usm.my
1. INTRODUCTION
The past few decades have seen a growing interest in autonomous mobile robots (AMRs) in both
industries and academia due to rapid technological advancements and the extensive use of robotics particularly
for reducing costs and enhancing productivity [1]-[4]. A two-wheeled self-balancing robot (TWSBR) is one
type of AMRs that is underactuated and inherently unstable but has notable advantages of being able to move
on a zero-radius curve, high tolerance to impulsive force, and small footprints to move in dangerous places. The
Segway Personal Transporter and Ninebot scooters are examples of commercialized technologies that apply the
same concept of a TWSBR. Apart from being an alternative mode of transportation that can take the place of
an automobile for short commutes, they have also been demonstrated useful for populations with a range of
functional disabilities [5]. Although the user safety cannot be totally guaranteed [6]-[8], these technologies are
often equipped with multiple control systems to enhance their reliability in the event of failure in any one of
them, which in turn results in a higher cost.
In academia, the TWSBR is often used as a research platform to verify advanced control algorithms
as its behaviour is comparable to that of the classical inverted pendulum system. Its wheels are usually driven
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2. 352 ❒ ISSN: 2089-4864
by an electrico-mechanical system which can be either direct current (DC) motors or stepper motors [9]. The
main control objective is to stabilize the robot by driving all the state variables, which are the robot’s position,
velocity, tilt angle and angular velocity, to approach their desired stable values in the shortest time possible. In
the TWSBR development, speed encoders, an accelerometer and a gyroscope are typically required to measure
these state variables [10]. Many techniques have been proposed to solve the problem in the literature, which can
be categorized into linear and nonlinear control approaches [11]-[14]. Examples of the latter include sliding
mode controls [15], fuzzy logic control [16], [17], artificial neural network [18], and deep learning [19]. Aside
from that, the Gaussian process (GP) has also been employed to its capability to create flexible nonlinear
nonparametric models [20]. Chen et al. [21] explained for instance, a control design based on a learned GP
regression model is proposed to alleviate the effects from modeling errors.
While the aforementioned nonlinear control methods have been demonstrated to provide robustness
against uncertainties, the resulting complexity will typically limit their applicability to low-cost embedded
controllers. The linear control approaches on the other hand are relatively simpler in terms of their imple-
mentations on hardware. The most popular methods are proportional-integral-derivative (PID) [22]-[24] and
linear quadratic regulator (LQR) schemes [25]. Nevertheless, a notable downside of the PID control algorithm
for controlling the TWSBR is the difficulty of parameter tuning [26], [27]. Although there are many software
tools available to aid the optimization of the PID parameters, the resulting control law may not be desirable
for the TWSBR which can be easily driven to the instability region. In contrast, controlling the TWSBR via
the LQR scheme is relatively more straightforward as its optimal parameters can be obtained by minimizing
the cost function that can also preserve the closed-loop stability at the same time. However, utilization of this
controller requires prior knowledge and skills in analysing the trade off between the control performance and
power consumption.
Most of the aforementioned work focused on developing new control strategies that can be validated
via simulations. In practice, the system is not only inherently nonlinear and unstable, but is prone to random
noise and disturbances [28]. Plus, there is no unique solution when it comes to hardware implementation as
the robot’s stability is highly dependent on the robustness of the electronic control systems. In this study, a
TWSBR that is controlled by an embedded NI myRIO-1900 board with LabVIEW-based control scheme is
developed. We compare the performance between PID and LQR schemes which are designed based on the
robot’s model that is constructed from Newtonian principles. A hybrid PID-LQR scheme is then proposed
to compensate for the individual components’ limitations. Experimental results demonstrate the PID is more
effective at regulating the tilt angle of the robot in the presence of external disturbances, but it necessitates a
higher velocity to sustain its equilibrium. The LQR on the other hand outperforms PID in terms of maximum
initial tilt angle. By combining both schemes, significant improvements can be observed, such as an increase
in maximum initial tilt angle and a reduction in settling time.
2. METHOD
2.1. TWSBR design and modeling
The TWSBR built in this study is shown in Figure 1 which consists of an NI myRIO-1900 board, two
brushed DC motors with encoders, an external gyroscope (i.e. PmodGyro) to improve the tilt angle estimation
(explained further in section 2.3), a 12 V Li-Ion power supply, a printed circuit board (PCB) containing the
motor driver and voltage regulators, the wheels and the chassis. Figure 1(a) illustrates the built TWSBR while,
Figure 1(b) visualizes the connection between each unit. The control scheme is constructed via model-based
design approach in LabVIEW which is also used as a user interface to display and log the data wirelessly during
the experiment.
The robot can be modeled from first principles by taking into account the dynamics of the motors,
robot chassis, and the forces on the wheels. The inputs to the TWSBR are the torques applied to the left and
right wheels, which are assumed to be similar. Figure 2 illustrates the free body diagram of the TWSBR. The
diagram for the robot’s wheel is depicted in Figure 2(a) where Tw denotes the torque applied to it, θ is the
angular position, Fw is the force applied to the wheel by the chassis, and Ff is the friction force by the surface
contact. With regard to the robot chassis which includes the NI-myRIO board, batteries, gyroscope and the
printed circuit board, it acts similar to an inverted pendulum as illustrated in Figure 2(b) whose base is attached
to the wheels.
Int J Reconfigurable Embedded Syst, Vol. 12, No. 3, November 2023: 351–359
3. Int J Reconfigurable Embedded Syst ISSN: 2089-4864 ❒ 353
(a) (b)
Figure 1. The TWSBR’s prototype built in (a) this study and (b) its connection diagram
(a) (b)
Figure 2. Free body diagrams of (a) the wheel and (b) the TWSBR’s chassis
Let Jw be the moment of inertia of the wheel, r be its radius, mw be mass of the wheel, and x be the
horizontal position of the center of the wheel relative to a defined origin. Using Newton’s law of motion, the
sum of forces in the horizontal x-direction can be written as (1):
mwẍ = Ff − Fw (1)
for each wheel. Assuming there are no tire deformation and rolling resistance, the sum of torques is given by
(2):
Jwθ̈ = Tw − Ff r (2)
for each wheel. From the DC motor dynamics, the torque relates to the input voltage as Tw = (km/R)Vin −
(kmke/R)θ̇, where R is the electrical resistance of the motor, and Vin is the applied voltage, and km and ke
are torque and back EMF constants respectively. Substituting this expression into (2), we will get (3).
Ff = (km/Rr)Vin − (kmke/Rr2
)ẋ − (Jw/r2
)ẍ. (3)
Replacing Ff in (1) with this expression gives (4).
Fw = (km/Rr)Vin − (kmke/Rr2
)ẋ − mw + (Jr/r2
)
ẍ (4)
The sum of forces in the x-direction can be written as (5):
Nx = mcẍ + mcℓβ̈ cos β − mcℓβ̇2
sin β (5)
where Nx is the combination of forces from both wheels, mc is the mass, ℓ is the distance to the center of the
mass, θ is the angle between vertical line and the pendulum, and ẍ is the robot’s acceleration in the x-direction.
The sum of forces perpendicular to the pendulum is simply:
Ny sin β + Nx cos β − mcg sin β − mcℓβ̈ = mcẍ cos β (6)
Self-balancing robot: modeling and comparative analysis between PID and linear quadratic ... (Lu Bin Lau)
4. 354 ❒ ISSN: 2089-4864
where Ny refers to the force in the y direction, and g refers to the gravity constant. The sum of torques acting
at the center of the pendulum is:
−Nyℓ sin β − Nxℓ cos β − 2Tw = Jcβ̈ (7)
where Jc is the pendulum’s moment of inertia. Assuming α is sufficiently small, we have sin β = sin(π+α) ≈
−α; cos β = cos(π + α) ≈ −1; β2
= α2
≈ 0, which lead to the following approximations.
Nx = mcẍ − mcℓα̈ (8)
−Nyα − Nx + mcgα − mcℓα̈ = −mcẍ (9)
Nyℓα + Nxℓ − 2Tw = Jcα̈ (10)
By substituting the expression of Tw into (10), we will obtain.
Nyℓα + Nxℓ −
2km
R
Vin +
2kmke
Rr
ẋ = Jcα̈ (11)
Let x1 = x, x2 = ẋ, x3 = α, x4 = α̇, u = Vin, and q = [x1 x2 x3 x4]T
, the state space representation
of the TWSBR can be constructed as q̇ = Aq + Bu; y = Cq with:
A =
0 1 0 0
0 −
2kmke(Jc + mcℓ2
− mcℓr)
ΩRr2
m2
cℓ2
g
Ω
0
0 0 0 1
0
2kmke(Λr − mcℓ)
ΩRr2
mcgℓΛ
Ω
0
; B =
0
2km(Jc + mcℓ2
− mcrℓ)
ΩRr
0
2km(mcℓ − Λr)
ΩRr
(12)
C =
0 0 1 0
(13)
2.2. Control schemes for TWSBR
Figure 3 illustrates the difference between the PID control (Figure 3(a)), LQR control (Figure 3(b))
and the PID-LQR scheme (Figure 3(c)) which is proposed to overcome the limitation of each component. For
the LQR control scheme, the control parameter KL =
k1 k2 k3 k4
is optimized by minimizing the
following cost function J =
R ∞
0
qT
Q + KT
L RKL
q dt where Q ≥ 0 and R 0. The optimal value of
KL can be obtained by using the formula KL = R−1
BT
P where P is the solution to the Ricatti equation
AT
P + PA − PBR−1
BT
P + Q = 0. With regard to the PID control scheme, the parameters for Kp, Ki and
Kd were tuned based on the Ziegler-Nichols approach as presented in [29].
(a) (b) (c)
Figure 3. Illustrations on (a) the LQR and PID, (b) the proposed hybrid PID-LQR, and (c) control schemes for
the TWSBR
2.3. Software implementation
In this work, the control schemes were designed in the LabVIEW software and deployed to the em-
bedded NI myRIO-1900 board. The board consists of a built-in accelerometer which provides the angular
acceleration from x,y, and z axes. The tilt angle from the accelerometer can be obtained as (14):
αacc = (arctan val(y)/val(z)
) × 180◦
/π (14)
Int J Reconfigurable Embedded Syst, Vol. 12, No. 3, November 2023: 351–359
5. Int J Reconfigurable Embedded Syst ISSN: 2089-4864 ❒ 355
where val(y) is the accelerometer value from the y-axis, val(z) is the accelerometer value from the z-axis,
and Ts is the sampling time. However, any shock or vibration will produce sudden spikes on the tilt angle
which can lead to instability of the robot. Therefore, the pitch angle of the robot was obtained through sensor
fusion by combining readings from the accelerometer and a gyroscrope, αgyro, which was externally connected
to the board. A low pass filter was applied to the accelerometer to attenuate the high frequency vibration
noise produced by the motors of the TWSBR. The drift from the PmodGyro’s readings was minimized by
applying a high pass filter. The filtered readings were then combined to obtain a more stable reading for the
TWSBR. The output of the sensor fusion block is considered as the actual tilt angle of the robot, i.e. α. The
corresponding angular velocity, α̇, of the robot can be obtained by a simple mathematical calculation, i.e.
α̇ = (α(k) − α(k − 1))/Ts.
To measure the robot’s position and speed, i.e. x and ẋ, the angular speeds of the DC motors were
firstly measured using hall-effect magnetic encoders. Since each of the hall sensors gives a resolution of 390
lines per revolution, quadruple outputs from the two hall sensors give an effective resolution of 1,560 lines per
revolution. Therefore, the angular speed (in rad/s) of each motor can be calculated as follows:
ωi = (C(k) − C(k − 1))/Ts × (2π/1560), i = R, L (15)
where C(k) is the encoder count at iteration k, and ωR(ωL) is the angular speed of the right (left) wheel. The
robot’s speed and position can then be written as follows: ẋ = (ωR + ωL)r/2 and x = ẋTs + x0 respectively
where x0 is the previous position. As the controller’s output will send a command that is linearly proportional
to the speed, a closed-loop speed control technique from [17] is employed to ensure the actual speed of the
robot follows the reference value.
3. RESULTS AND DISCUSSION
Based on the TWSBR model parameters in Table 1, the resulting transfer function Gα is:
Gα =
39.77s
s3 + 66.1s2 − 0.514s − 26.88
,
and the A and B matrices are:
A =
0 1 0 0
0 −1.1237 6.9748 0
0 0 0 1
0 −255.26 128.9962 0
; B =
0
10.30
0
39.77
. (16)
Table 1. Model parameters of the robot
Notation Definition Value/unit
g Gravitational acceleration 9.81 ms−2
mc Mass of the chassis 0.7604 kg
mw Mass of each wheel 0.048 kg
Jc Moment of inertia of the chassis 0.0032 kgm2
Jw Moment of inertia of the wheel 0.000074 kgm2
R Electrical resistance of the motor 1.6 Ω
r Radius of the wheel 0.034 m
km Motor torque constant 0.2182 Nm/A
ke Back EMF constant 0.2182 V/(rad/s)
ℓ Half length of the chassis 0.0 m
To accurately model the built robot, its linear speed is constrained within ±100 cm/s. Using the LQR
control design techniques in section 2.2, the values of Q and R were set to Q = diag(10, 10, 0, 0) and R = 1
respectively to give the following state feedback gain KL =
−3.1623 6.9428 −3.9015 −0.4482
. For
the PID, the optimal values obtained were Kp = 300; Ki = 5500; Kd = 1.5. A filter coefficient of
10, 000 was included in the PID control scheme to make the transfer function realizable. In the proposed hybrid
method, some parameter tunings need to be performed to maintain the stability and improve the performance of
Self-balancing robot: modeling and comparative analysis between PID and linear quadratic ... (Lu Bin Lau)
6. 356 ❒ ISSN: 2089-4864
the closed-loop system. The optimal values obatined were K̂L = [−207.84 245.64 − 220.47 − 18.25],
K̂p = 0.1, K̂i = 0.3 and K̂d = 0.7.
In order to evaluate the performance of the three control schemes on the TWSBR, they were tested
experimentally when the robot was placed on two types of surface, i.e. rough and smooth. For each scheme,
the initial tilt angle was slowly increased after each trial until the controller was no longer able to maintain the
stability of the TWSBR. To provide a fair comparison, only the maximum initial tilt angle that the robot was
able to self-balance and its corresponding settling time were used to evaluate the performance since the noise
and disturbances entering the system were random and unmeasurable. The settling time in this case is defined
as the time it takes for the tilt angle to reach within ±1◦
region.
Table 2 records the numerical results while Figure 4 to Figure 6 illustrate the performance of the
TWSBR with PID, LQR, and hybrid PID-LQR control schemes on both surfaces. Durations of oscillations
and settling time are seen longer on smooth surface for all control schemes due to a higher probability of the
robot to have wheel slips. Comparing Figure 4(a)-Figure 4(d) against Figure 5(a)-Figure 5(d), the PID is seen
to be more effective at regulating the tilt angle of the robot, but it necessitates a higher linear velocity (i.e. back
and forth) to sustain its equilibrium as can be observed from the trajectories of x2 in Figure 4(a), Figure 4(c),
Figure 5(a), and Figure 5(c). The LQR on the other hand outperforms the PID in terms of both maximum tilt
angle and settling time. Nonetheless, a significant improvement is achieved when both schemes are hybridized
as can be observed from Figure 6 and the last column in Table 2 where the settling time is reduced and the
maximum initial tilt angle is increased (Figure 6(a) and Figure 6(c)). Figure 6(b) and Figure 6(d) also show a
considerable reduction in the amplitudes of x3 and x4.
Table 2. Performance evaluations between PID, LQR, and hybrid LQR-PID schemes by experiments
Control scheme
Type of surface Performance metric PID LQR Hybrid
Rough Maximum initial tilt angle (deg) 2.01 7.42 9.22
Settling time (s) 2.78 2.12 2.04
Smooth Maximum initial tilt angle (deg) 1.04 3.21 10.11
Settling time (s) 3.02 2.13 2.12
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-30
-20
-10
0
10
20
30
x
1
(cm),
x
2
(cm/s)
Performance with PID on Rough Surface
x
1
x2
(a)
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-10
-8
-6
-4
-2
0
2
4
6
8
10
x
3
(deg),
x
4
(deg/s)
Performance with PID on Rough Surface
x
3
x4
(b)
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-30
-20
-10
0
10
20
30
x
1
(cm),
x
2
(cm/s)
Performance with PID on Smooth Surface
x1
x
2
(c)
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-10
-8
-6
-4
-2
0
2
4
6
8
10
x
3
(deg),
x
4
(deg/s)
Performance with PID on Smooth Surface
x3
x
4
(d)
Figure 4. Performance of the TWSBR with PID control in terms of trajectories of (a) x1, x2 on a rough
surface, (b) x3, x4 on a rough surface, (c) x1, x2 on a smooth surface, and (d) x3, x4 on a smooth surface
Int J Reconfigurable Embedded Syst, Vol. 12, No. 3, November 2023: 351–359
7. Int J Reconfigurable Embedded Syst ISSN: 2089-4864 ❒ 357
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-30
-20
-10
0
10
20
30
x
1
(cm),
x
2
(cm/s)
Performance with LQR on Rough Surface
x1
x2
(a)
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-10
-8
-6
-4
-2
0
2
4
6
8
10
x
3
(deg),
x
4
(deg/s)
Performance with LQR on Rough Surface
x3
x4
(b)
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-30
-20
-10
0
10
20
30
x
1
(cm),
x
2
(cm/s)
Performance with LQR on Smooth Surface
x1
x
2
(c)
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-10
-8
-6
-4
-2
0
2
4
6
8
10
x
3
(deg),
x
4
(deg/s)
Performance with LQR on Smooth Surface
x3
x
4
(d)
Figure 5. Performance of the TWSBR with LQR control in terms of trajectories of (a) x1, x2 on a rough
surface, (b) x3, x4 on a rough surface, (c) x1, x2 on a smooth surface, and (d) x3, x4 on a smooth surface
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-30
-20
-10
0
10
20
30
x
1
(cm),
x
2
(cm/s)
Performance with Hybrid PID-LQR on Rough Surface
x
1
x2
(a)
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-10
-8
-6
-4
-2
0
2
4
6
8
10
x
3
(deg),
x
4
(deg/s)
Performance with Hybrid PID-LQR on Rough Surface
x
3
x4
(b)
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-30
-20
-10
0
10
20
30
x
1
(cm),
x
2
(cm/s)
Performance with Hybrid PID-LQR on Smooth Surface
x
1
x2
(c)
0 0.5 1 1.5 2 2.5 3 3.5
Time (s)
-10
-8
-6
-4
-2
0
2
4
6
8
10
x
3
(deg),
x
4
(deg/s)
Performance with Hybrid PID-LQR on Smooth Surface
x
3
x4
(d)
Figure 6. Performance of the TWSBR with a hybrid PID-LQR in terms of trajectories of (a) x1, x2 on a rough
surface, (b) x3, x4 on a rough surface, (c) x1, x2 on a smooth surface, and (d) x3, x4 on a smooth surface
Self-balancing robot: modeling and comparative analysis between PID and linear quadratic ... (Lu Bin Lau)
8. 358 ❒ ISSN: 2089-4864
4. CONCLUSION
In this study, we developed a TWSBR controlled by an embedded NI myRIO-1900 board with a
model-based control scheme. Our experimental results showed that PID is more effective in regulating the
robot’s tilt angle in the presence of external disturbances, but it requires a higher velocity to maintain equilib-
rium. On the other hand, LQR outperforms PID in terms of the maximum initial tilt angle. By combining both
schemes, we observed significant improvements, such as an increase in the maximum initial tilt angle and a re-
duction in settling time. While the experimental results presented in this study provide valuable insights into the
performance of the TWSBR, future research could focus on evaluating its performance in real-world scenarios,
such as navigating through complex environments or performing specific tasks. Machine learning techniques,
such as reinforcement learning, could also be used to train the TWSBR to adapt to changing environmental
conditions or to optimize its performance based on specific performance criteria.
ACKNOWLEDGEMENT
The authors would like to thank Ministry of Higher Education Malaysia for the financial support under
Fundamental Research Grant Scheme with Project Code: FRGS/1/2021/TK0/USM/02/18.
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BIOGRAPHIES OF AUTHORS
Lu Bin Lau was born in Penang in 1998. He received the B.Eng. degree in electronic
engineering from School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM)
in 2022. His research interest centers around control systems and robotics. During his undergraduate
studies at USM, he was actively involved in various national and international robotics competitions.
He joined National Instruments as an intern in 2021, and is currently working as an electrical engineer.
He can be contacted at email: laulubin@student.usm.my.
Nur Syazreen Ahmad received the B.Eng. degree in electrical and electronic engineer-
ing from the University of Manchester, United Kingdom and Ph.D. degree in control systems from
the same university. She is currently an associate professor at the School of Electrical and Elec-
tronic Engineering, University Sains Malaysia (USM), specializing in embedded control systems,
sensor networks and mobile robotics. Her main research interest revolves around autonomous mo-
bile robots, with a particular focus on sensing, identification, intelligent control and indoor naviga-
tion. She is a member of the IEEE Young Professional and Control System societies, and has gained
a recognition as a certified LabVIEW Associate Developer by NI. She can be contacted at email:
syazreen@usm.my.
Patrick Goh received the B.S., M.S., and Ph.D. degrees in electrical engineering from the
University of Illinois at Urbana-Champaign, Urbana, IL, USA in 2007, 2009, and 2012 respectively.
Since 2012, he has been with the School of Electrical and Electronic Engineering, Universiti Sains
Malaysia, where he currently specializes in the study of signal integrity for high-speed digital designs.
His research interest includes the development of circuit simulation algorithms for computer-aided
design tools. He was a recipient of the Raj Mittra Award in 2012 and the Harold L. Olesen Award
in 2010, and has served on the technical program committee and international program committee
in various IEEE and non-IEEE conferences around the world. He can be contacted at email: eep-
atrick@usm.my.
Self-balancing robot: modeling and comparative analysis between PID and linear quadratic ... (Lu Bin Lau)