This project was developed for an Embedded systems class: we implemented a PID controller for a mechanical inverted pendulum. It was very interesting to experiment in practice with a simple control plant.
Mathematical model analysis and control algorithms design based on state feed...hunypink
XZ-Ⅱtype rotary inverted pendulum is a typical mechatronic system; it completes real-time motion control using DSP motion controller and motor torque. In this paper, we recognize XZ-Ⅱrotational inverted pendulum and learn system composition, working principle, using method, precautions and software platform. We master how to build mathematical model and state feedback control method (pole assignment algorithm) of the one order rotational inverted pendulum system and finish simulation study of system using Mat lab. In the end we grasp debugging method of the actual system, and finish online control of the one order rotational inverted pendulum system as well.
Iaetsd modelling and controller design of cart inverted pendulum system using...Iaetsd Iaetsd
This document presents a model reference adaptive control (MRAC) scheme for stabilizing a cart-inverted pendulum system. The cart-inverted pendulum is a highly nonlinear and unstable system that is challenging to control. The proposed controller uses Lyapunov stability theory to design an MRAC controller. Simulation results show the controller is able to balance the inverted pendulum in the unstable upright position and regulate the cart position, demonstrating the effectiveness of the proposed MRAC control approach.
MODELLING AND SIMULATION OF INVERTED PENDULUM USING INTERNAL MODEL CONTROLJournal For Research
The internal model control (IMC) philosophy relies on the internal model principle, which states that control can be achieved only if the control system encapsulates, either implicitly or explicitly, some representation of the process to be controlled. In particular, if the control scheme is developed based on an exact model of the process, then perfect control is theoretically possible. Transfer function of Inverted Pendulum is selected as the base of design, which examines IMC controller. Matlab/simulink is used to simulate the procedures and validate the performance. The results shows robustness of the IMC and got graded responses when compared with PID. Furthermore, a comparison between the PID and IMC was shows that IMC gives better response specifications.
1) The document provides an overview of inverted pendulum control, focusing on mobile inverted pendulums.
2) It describes the structure of a mobile inverted pendulum system with a cart and mounted pendulum. Equations of motion are provided.
3) Two common control strategies for inverted pendulums are discussed: PID control and fuzzy logic control. Performance comparisons using simulations show fuzzy logic control provides better response.
This document describes the design and implementation of a controller for an inverted pendulum on a cart system. It provides the nonlinear and linearized models of the system and designs a PID controller using root locus analysis. Simulation results show the uncompensated system is unstable but the controlled system with PID controller and pre-compensator meets design specifications with less than 0.2 seconds settling time and 8% overshoot for a unit step input.
1) An LQR controller with feedforward control and steady state error tracking was designed and simulated to control an inverted pendulum system.
2) The LQR controller stabilized the unstable system and achieved good performance for the pendulum angle and cart position with minimal overshoot and steady state error.
3) Simulation results demonstrated the robustness of the designed controller under system uncertainties, showing improved performance over existing H-infinity control methods.
Controller design of inverted pendulum using pole placement and lqreSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document summarizes a student project on stabilizing and balancing linear and rotary inverted pendulum systems. It discusses the design and implementation of PID controllers to balance an inverted pendulum mounted on a cart (linear system) and a rotary inverted pendulum prototype. Key steps included mathematical modeling, simulation in MATLAB, PID controller tuning, and applying the controller to experimental setups. Results showed the systems could be stabilized using optimized PID and LQR controllers designed via pole placement and minimizing cost functions.
Mathematical model analysis and control algorithms design based on state feed...hunypink
XZ-Ⅱtype rotary inverted pendulum is a typical mechatronic system; it completes real-time motion control using DSP motion controller and motor torque. In this paper, we recognize XZ-Ⅱrotational inverted pendulum and learn system composition, working principle, using method, precautions and software platform. We master how to build mathematical model and state feedback control method (pole assignment algorithm) of the one order rotational inverted pendulum system and finish simulation study of system using Mat lab. In the end we grasp debugging method of the actual system, and finish online control of the one order rotational inverted pendulum system as well.
Iaetsd modelling and controller design of cart inverted pendulum system using...Iaetsd Iaetsd
This document presents a model reference adaptive control (MRAC) scheme for stabilizing a cart-inverted pendulum system. The cart-inverted pendulum is a highly nonlinear and unstable system that is challenging to control. The proposed controller uses Lyapunov stability theory to design an MRAC controller. Simulation results show the controller is able to balance the inverted pendulum in the unstable upright position and regulate the cart position, demonstrating the effectiveness of the proposed MRAC control approach.
MODELLING AND SIMULATION OF INVERTED PENDULUM USING INTERNAL MODEL CONTROLJournal For Research
The internal model control (IMC) philosophy relies on the internal model principle, which states that control can be achieved only if the control system encapsulates, either implicitly or explicitly, some representation of the process to be controlled. In particular, if the control scheme is developed based on an exact model of the process, then perfect control is theoretically possible. Transfer function of Inverted Pendulum is selected as the base of design, which examines IMC controller. Matlab/simulink is used to simulate the procedures and validate the performance. The results shows robustness of the IMC and got graded responses when compared with PID. Furthermore, a comparison between the PID and IMC was shows that IMC gives better response specifications.
1) The document provides an overview of inverted pendulum control, focusing on mobile inverted pendulums.
2) It describes the structure of a mobile inverted pendulum system with a cart and mounted pendulum. Equations of motion are provided.
3) Two common control strategies for inverted pendulums are discussed: PID control and fuzzy logic control. Performance comparisons using simulations show fuzzy logic control provides better response.
This document describes the design and implementation of a controller for an inverted pendulum on a cart system. It provides the nonlinear and linearized models of the system and designs a PID controller using root locus analysis. Simulation results show the uncompensated system is unstable but the controlled system with PID controller and pre-compensator meets design specifications with less than 0.2 seconds settling time and 8% overshoot for a unit step input.
1) An LQR controller with feedforward control and steady state error tracking was designed and simulated to control an inverted pendulum system.
2) The LQR controller stabilized the unstable system and achieved good performance for the pendulum angle and cart position with minimal overshoot and steady state error.
3) Simulation results demonstrated the robustness of the designed controller under system uncertainties, showing improved performance over existing H-infinity control methods.
Controller design of inverted pendulum using pole placement and lqreSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This document summarizes a student project on stabilizing and balancing linear and rotary inverted pendulum systems. It discusses the design and implementation of PID controllers to balance an inverted pendulum mounted on a cart (linear system) and a rotary inverted pendulum prototype. Key steps included mathematical modeling, simulation in MATLAB, PID controller tuning, and applying the controller to experimental setups. Results showed the systems could be stabilized using optimized PID and LQR controllers designed via pole placement and minimizing cost functions.
This document summarizes a project to stabilize an inverted pendulum using a lead-lag compensator. It includes the mathematical modeling of the inverted pendulum system and motor cart dynamics. The transfer functions of the individual systems and overall plant are derived. Root locus analysis is used to design the compensator. An analog to digital converter and parallel port are used to interface the hardware and send sensor readings to the computer for processing. References on control systems and inverted pendulum simulations are also provided.
This document is the final project report for controlling an inverted pendulum system. It includes modeling the nonlinear dynamics of the pendulum cart system and deriving the state space equations. The goal is to balance the pendulum in the vertically upward unstable equilibrium position using feedback control. The report outlines modeling the system, linearizing about the unstable point, designing a feedback controller using linear quadratic regulation, and simulating the closed-loop response. Parameter perturbations are also analyzed through simulation to study the transient behavior and stability margins of the controlled system.
This document describes the modeling and simulation of an inverted pendulum system. It begins with deriving the nonlinear equations of motion for an inverted pendulum mounted on a moving cart. It then linearizes the model around the equilibrium point and simulates both the linear and nonlinear models. Various controller designs are tested, including state feedback, PID control, and using position of the cart and pendulum as feedback. The linear model is shown to approximate the nonlinear model well. Increased mass or length are found to decrease stability. PID control is optimized by tuning gains.
The stabilization of forced inverted pendulum via fuzzy controllereSAT Journals
Abstract
In the field of nonlinear control engineering, the inverted pendulum can be considered as a bench mark problem. For an inverted
pendulum, there are mainly two types of equilibrium which are categorized as stable equilibrium and unstable equilibrium. The
stable equilibrium is the one in which the pendulum is in normal pendent position and not requires any control force since
because it is naturally stable. Under the influence of an external force, the stable equilibrium loses its stability and there comes
the need of a stabilizing controller. Therefore unstable equilibrium refers to the pendulum in upright position strictly under the
influence of a stabilizing controller. The inverted pendulum is strictly nonlinear, under actuated system; challenging task comes
with the stability analysis. A forced inverted pendulum is considered which has been modeled with respect to the cart motion. To
improve the performance and stabilize the system, a fuzzy controller is designed for the respective system. Simulation results
validate the fact that the stabilization is achieved through out and the perfect result is obtained for the system.
Keyword: Fuzzy, Heuristic, Forced Inverted Pendulum
2_DOF_Inverted_Pendulum_Laboratory_SessionPeixi Gong
This document provides an introduction and overview of a lab session on controlling a 2-DOF inverted pendulum system. It describes the equipment, typical steps in the control project including modeling and controller design. It also presents the nonlinear and linearized mathematical models of the system and exercises for students to analyze stability, observability and derive the state space models.
Consider the following nonlinear system:
dx/dt = f(x) + g(x)u
Where x is an n-dimensional vector and f and g are sufficiently smooth vector fields.
The goal is to design a feedback control law u = α(x) that renders the origin globally asymptotically stable (GAS).
Backstepping provides a systematic approach to solve this problem by considering the system as a cascade of "pseudo" linear systems with intermediate virtual controls.
The procedure recursively constructs stabilizing functions and control laws to backstep through this cascade until the actual control input is determined.
This approach systematically cancels out the nonlinearities in f while preserving the desirable properties introduced by g
This document analyzes the problem of balancing an inverted pendulum, where a steel ball rolls on arched tracks attached to a movable cart. It describes the control objective of keeping the ball balanced on top of the arc while positioning the cart. The key points are:
1) The problem is modeled using basic physical equations accounting for the vertical and horizontal reaction forces on the ball and cart.
2) The equations are nonlinear and coupled, but can be linearized around the origin for control purposes.
3) State feedback control is implemented using linearized model parameters to feed back the four states to the controller.
4) Cascade control divides the problem into inner-loop ball control and outer-loop cart
Simulation of inverted pendulum presentationPourya Parsa
This document presents a simulation of controlling an inverted pendulum. It includes equations of motion to model the system state based on the pendulum angle and position over time. A sliding control method is used to control the pendulum angle and ride height, choosing control inputs to minimize errors between the actual and desired states. Simulation results are shown controlling the pendulum velocity and angle to stabilize the system. An animation demonstrates the full simulated control of the inverted pendulum.
The document describes a project to create a quasi-equilibrium state pendulum using a DC motor attached to a rod. The motor drives a propeller to allow the rod to swing. Angular position is measured by a potentiometer. The system is modeled and controlled using Scilab/Xcos. Initial PID tuning is done using Ziegler-Nichols method. Future work proposed includes expanding the controllable angle range and implementing advanced controllers like adaptive control to improve response. The appendix lists components used and conclusions reiterate the potential applications and limitations of PID control for this system.
Linear quadratic regulator and pole placement for stabilizing a cart inverted...journalBEEI
The system of a cart inverted pendulum has many problems such as nonlinearity, complexity, unstable, and underactuated system. It makes this system be a benchmark for testing many control algorithm. This paper presents a comparison between 2 conventional control methods consist of a linear quadratic regulator (LQR) and pole placement. The comparison indicated by the most optimal steps and results in the system performance that obtained from each method for stabilizing a cart inverted pendulum system. A mathematical model of DC motor and mechanical transmission are included in a mathematical model to minimize the realtime implementation problem. From the simulation, the obtained system performance shows that each method has its advantages, and the desired pendulum angle and cart position reached.
Robust control theory based performance investigation of an inverted pendulum...Mustefa Jibril
This document describes a study investigating the performance of an inverted pendulum system using robust control theory. Two controllers - H∞ mixed sensitivity and H∞ loop shaping using Glover McFarlane method - are designed and their performance compared in simulations. The inverted pendulum with the mixed sensitivity controller showed smaller rise time, settling time and overshoot for step responses, as well as better impulse responses. Overall the mixed sensitivity controller provided the best performance in simulations.
Attitude Control of Satellite Test Setup Using Reaction WheelsA. Bilal Özcan
This document summarizes a presentation about attitude control of a satellite test setup using reaction wheels. It describes the mathematical models of DC motors, reaction wheels, and the satellite test setup. It also discusses the implementation of a PID controller to control the satellite's orientation by generating angular velocity references for the reaction wheels. Simulation results show that the settling time of the system was decreased from 21.5 seconds to 6.1 seconds by optimizing the PID gains. Future work is planned to consider effects like vibrations and actuator saturations when testing the system.
This document discusses model reference adaptive control (MRAC). It provides an overview of the concept, the MIT rule for updating controller parameters, and an example of applying MRAC to control the position of a pendulum. Simulation and experimental results show the controller requires proportional-derivative feedback and tuning to stabilize the unstable pendulum system. More advanced control methods could provide better practical performance than the basic MRAC approach presented.
Comparative analysis of observer-based LQR and LMI controllers of an inverted...journalBEEI
An inverted pendulum is a multivariable, unstable, nonlinear system that is used as a yardstick in control engineering laboratories to study, verify and confirm innovative control techniques. To implement a simple control algorithm, achieve upright stabilization and precise tracking control under external disturbances constitutes a serious challenge. Observer-based linear quadratic regulator (LQR) controller and linear matrix inequality (LMI) are proposed for the upright stabilization of the system. Simulation studies are performed using step input magnitude, and the results are analyzed. Time response specifications, integral square error (ISE), integral absolute error (IAE) and mean absolute error (MAE) were employed to investigate the performances of the proposed controllers. Based on the comparative analysis, the upright stabilization of the pendulum was achieved within the shortest possible time with both controllers however, the LMI controller exhibits better performances in both stabilization and robustness. Moreover, the LMI control scheme is effective and simple.
This work treats the modeling and simulation of non-linear system behavior of an induction motor using backstepping sliding mode control (BACK- SMC). First, the direct field oriented control IM is derived. Then, a sliding for direct field oriented control is proposed to compensate the uncertainties, which occur in the control. Finally, the study of Backstepping sliding controls strategy of the induction motor drive. Our non linear system is simulated in MATLAB SIMULINK environment, the results obtained illustrate the efficiency of the proposed control with no overshoot, and the rising time is improved with good disturbances rejections comparing with the classical control law.
Stabilized controller of a two wheels robotjournalBEEI
The Segway Human Transport (HT) robot, it is dynamical self-balancing robot type. The stability control is an important thing for the Segway robot. It is an indisputable fact that Segway robot is a natural instability framework robot. The case study of the Segway robot focuses on running balance control systems. The roll, pitch, and yaw balance of this robot are obtained by estimating the Kalman Filter with a combination of the pole placement and the Linear Quadratic Regulator (LQR) control method. In our system configuration, the mathematical model of the robot will be proved by Matlab Simulink by modelling of the stabilizing control system of all state variable input. Furthermore, the implementation of this system modelled to the real-time test of the Segway robot. The expected result is by substitute the known parameters from Gyro, Accelero and both rotary encoder to initial stabilize control function, the system will respond to the zero input curve. The coordinate units of displacement response and inclination response pictures are the same. As our expected, the response of the system can reach the zero point position.
Modelling and Control of Ground Test Set-up of Attitude of SatelliteA. Bilal Özcan
In this study, the first simulation study of a laboratory product, in which attitude control of the nanosatellite will be made with 4 reaction wheels and inertia sensor placed on the nanosatellite prototype, different controllers will be designed by the user and tested in the simulation environment, and the simulation results will be verified with the experiments on the real system.
Within the scope of the simulation study, the dynamic and kinematic equations of the system, the motor’s mathematical model, and the mixer system that gives the necessary voltage to the motors are modeled. The nonlinear satellite ground test system was controlled in 3-axes utilizing the PID controller according to the criteria selected in the time domain, and the attitude control of the nonlinear system was achieved.
Link: https://www.researchgate.net/publication/354385104_Modelling_and_Control_of_Ground_Test_Set-up_of_Attitude_of_Satellite_Uydu_Yonelimi_Yer_Test_Duzeneginin_Modellemesi_ve_Kontrolu
Intermittent predictive control of an inverted pendulumIvan Tim Oloya
The Rotary Inverted Pendulum (RIP) represents a broad class of under actuated sys-tems making its control a classic problem. The dynamic equations used to represent the RIP are complex and nonlinear, which makes design and control of the system challenging.
This paper presents swing up and stabilization of the RIP system. The controller used to achieve swing up is energy based and acts by adding energy to the system until the pendulum reaches the linear region in the vertical upright position. A high gain ob-server has been implemented to estimate the unmeasurable system states during swing up.
Once in the linear region, a stabilizing control is switched on. The switch on is made possible by designing a mode-switching strategy to determine the point at which tran-sition occurs. A linearized model of the RIP is used to determine the feedback gains needed for stabilization by applying the Linear-Quadratic Regulator (LQR) method.
Two different stabilizing controllers are compared. An intermittent controller, which can be either time-triggered or event-triggered and a continuous predictive controller. A linear Luenberger observer has been designed to estimate the unmeasurable system states when the pendulum has switched to stabilization control.
Effectiveness of this system has been verified through simulation using MATLAB and Simulink.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document summarizes a research paper on designing a composite robotic controller using reduced order observer, output feedback, and LQR control methods.
[1] It develops an efficient method to linearize the dynamic model of a two-link robot manipulator around a linear trajectory using Lagrangian mechanics and LQR control. [2] The controller uses state feedback, output feedback, and a reduced order observer to estimate immeasurable states and make the system sensorless. [3] Computer simulations show the proposed controller provides good trajectory tracking and improved state estimation for the two-link manipulator.
Real Time Implementation of Fuzzy Adaptive PI-sliding Mode Controller for Ind...IJECEIAES
In this work, a fuzzy adaptive PI-sliding mode control is proposed for Induction Motor speed control. First, an adaptive PI-sliding mode controller with a proportional plus integral equivalent control action is investigated, in which a simple adaptive algorithm is utilized for generalized soft-switching parameters. The proposed control design uses a fuzzy inference system to overcome the drawbacks of the sliding mode control in terms of high control gains and chattering to form a fuzzy sliding mode controller. The proposed controller has implemented for a 1.5kW three-Phase IM are completely carried out using a dSPACE DS1104 digital signal processor based real-time data acquisition control system, and MATLAB/Simulink environment. Digital experimental results show that the proposed controller can not only attenuate the chattering extent of the adaptive PI-sliding mode controller but can provide high-performance dynamic characteristics with regard to plant external load disturbance and reference variations.
Optimal and pid controller for controlling camera’s position in unmanned aeri...Zac Darcy
This paper describes two controllers designed specifically for adjusting camera’s position in a small unmanned aerial vehicle (UAV). The optimal controller was designed and first simulated by using MATLAB technique and the results displayed graphically, also PID controller was designedand simulatedby using MATLAB technique .The goal of this paper is to connect the tow controllers in cascade mode to obtain the desired performance and correction in camera’s position in both roll and pitch.
Optimal and Pid Controller for Controlling Camera's Position InUnmanned Aeria...Zac Darcy
This paper describes two controllers designed specifically for adjusting camera’s position in a small
unmanned aerial vehicle (UAV). The optimal controller was designed and first simulated by using
MATLAB technique and the results displayed graphically, also PID controller was designedand
simulatedby using MATLAB technique .The goal of this paper is to connect the tow controllers in cascade
mode to obtain the desired performance and correction in camera’s position in both roll and pitch.
This document summarizes a project to stabilize an inverted pendulum using a lead-lag compensator. It includes the mathematical modeling of the inverted pendulum system and motor cart dynamics. The transfer functions of the individual systems and overall plant are derived. Root locus analysis is used to design the compensator. An analog to digital converter and parallel port are used to interface the hardware and send sensor readings to the computer for processing. References on control systems and inverted pendulum simulations are also provided.
This document is the final project report for controlling an inverted pendulum system. It includes modeling the nonlinear dynamics of the pendulum cart system and deriving the state space equations. The goal is to balance the pendulum in the vertically upward unstable equilibrium position using feedback control. The report outlines modeling the system, linearizing about the unstable point, designing a feedback controller using linear quadratic regulation, and simulating the closed-loop response. Parameter perturbations are also analyzed through simulation to study the transient behavior and stability margins of the controlled system.
This document describes the modeling and simulation of an inverted pendulum system. It begins with deriving the nonlinear equations of motion for an inverted pendulum mounted on a moving cart. It then linearizes the model around the equilibrium point and simulates both the linear and nonlinear models. Various controller designs are tested, including state feedback, PID control, and using position of the cart and pendulum as feedback. The linear model is shown to approximate the nonlinear model well. Increased mass or length are found to decrease stability. PID control is optimized by tuning gains.
The stabilization of forced inverted pendulum via fuzzy controllereSAT Journals
Abstract
In the field of nonlinear control engineering, the inverted pendulum can be considered as a bench mark problem. For an inverted
pendulum, there are mainly two types of equilibrium which are categorized as stable equilibrium and unstable equilibrium. The
stable equilibrium is the one in which the pendulum is in normal pendent position and not requires any control force since
because it is naturally stable. Under the influence of an external force, the stable equilibrium loses its stability and there comes
the need of a stabilizing controller. Therefore unstable equilibrium refers to the pendulum in upright position strictly under the
influence of a stabilizing controller. The inverted pendulum is strictly nonlinear, under actuated system; challenging task comes
with the stability analysis. A forced inverted pendulum is considered which has been modeled with respect to the cart motion. To
improve the performance and stabilize the system, a fuzzy controller is designed for the respective system. Simulation results
validate the fact that the stabilization is achieved through out and the perfect result is obtained for the system.
Keyword: Fuzzy, Heuristic, Forced Inverted Pendulum
2_DOF_Inverted_Pendulum_Laboratory_SessionPeixi Gong
This document provides an introduction and overview of a lab session on controlling a 2-DOF inverted pendulum system. It describes the equipment, typical steps in the control project including modeling and controller design. It also presents the nonlinear and linearized mathematical models of the system and exercises for students to analyze stability, observability and derive the state space models.
Consider the following nonlinear system:
dx/dt = f(x) + g(x)u
Where x is an n-dimensional vector and f and g are sufficiently smooth vector fields.
The goal is to design a feedback control law u = α(x) that renders the origin globally asymptotically stable (GAS).
Backstepping provides a systematic approach to solve this problem by considering the system as a cascade of "pseudo" linear systems with intermediate virtual controls.
The procedure recursively constructs stabilizing functions and control laws to backstep through this cascade until the actual control input is determined.
This approach systematically cancels out the nonlinearities in f while preserving the desirable properties introduced by g
This document analyzes the problem of balancing an inverted pendulum, where a steel ball rolls on arched tracks attached to a movable cart. It describes the control objective of keeping the ball balanced on top of the arc while positioning the cart. The key points are:
1) The problem is modeled using basic physical equations accounting for the vertical and horizontal reaction forces on the ball and cart.
2) The equations are nonlinear and coupled, but can be linearized around the origin for control purposes.
3) State feedback control is implemented using linearized model parameters to feed back the four states to the controller.
4) Cascade control divides the problem into inner-loop ball control and outer-loop cart
Simulation of inverted pendulum presentationPourya Parsa
This document presents a simulation of controlling an inverted pendulum. It includes equations of motion to model the system state based on the pendulum angle and position over time. A sliding control method is used to control the pendulum angle and ride height, choosing control inputs to minimize errors between the actual and desired states. Simulation results are shown controlling the pendulum velocity and angle to stabilize the system. An animation demonstrates the full simulated control of the inverted pendulum.
The document describes a project to create a quasi-equilibrium state pendulum using a DC motor attached to a rod. The motor drives a propeller to allow the rod to swing. Angular position is measured by a potentiometer. The system is modeled and controlled using Scilab/Xcos. Initial PID tuning is done using Ziegler-Nichols method. Future work proposed includes expanding the controllable angle range and implementing advanced controllers like adaptive control to improve response. The appendix lists components used and conclusions reiterate the potential applications and limitations of PID control for this system.
Linear quadratic regulator and pole placement for stabilizing a cart inverted...journalBEEI
The system of a cart inverted pendulum has many problems such as nonlinearity, complexity, unstable, and underactuated system. It makes this system be a benchmark for testing many control algorithm. This paper presents a comparison between 2 conventional control methods consist of a linear quadratic regulator (LQR) and pole placement. The comparison indicated by the most optimal steps and results in the system performance that obtained from each method for stabilizing a cart inverted pendulum system. A mathematical model of DC motor and mechanical transmission are included in a mathematical model to minimize the realtime implementation problem. From the simulation, the obtained system performance shows that each method has its advantages, and the desired pendulum angle and cart position reached.
Robust control theory based performance investigation of an inverted pendulum...Mustefa Jibril
This document describes a study investigating the performance of an inverted pendulum system using robust control theory. Two controllers - H∞ mixed sensitivity and H∞ loop shaping using Glover McFarlane method - are designed and their performance compared in simulations. The inverted pendulum with the mixed sensitivity controller showed smaller rise time, settling time and overshoot for step responses, as well as better impulse responses. Overall the mixed sensitivity controller provided the best performance in simulations.
Attitude Control of Satellite Test Setup Using Reaction WheelsA. Bilal Özcan
This document summarizes a presentation about attitude control of a satellite test setup using reaction wheels. It describes the mathematical models of DC motors, reaction wheels, and the satellite test setup. It also discusses the implementation of a PID controller to control the satellite's orientation by generating angular velocity references for the reaction wheels. Simulation results show that the settling time of the system was decreased from 21.5 seconds to 6.1 seconds by optimizing the PID gains. Future work is planned to consider effects like vibrations and actuator saturations when testing the system.
This document discusses model reference adaptive control (MRAC). It provides an overview of the concept, the MIT rule for updating controller parameters, and an example of applying MRAC to control the position of a pendulum. Simulation and experimental results show the controller requires proportional-derivative feedback and tuning to stabilize the unstable pendulum system. More advanced control methods could provide better practical performance than the basic MRAC approach presented.
Comparative analysis of observer-based LQR and LMI controllers of an inverted...journalBEEI
An inverted pendulum is a multivariable, unstable, nonlinear system that is used as a yardstick in control engineering laboratories to study, verify and confirm innovative control techniques. To implement a simple control algorithm, achieve upright stabilization and precise tracking control under external disturbances constitutes a serious challenge. Observer-based linear quadratic regulator (LQR) controller and linear matrix inequality (LMI) are proposed for the upright stabilization of the system. Simulation studies are performed using step input magnitude, and the results are analyzed. Time response specifications, integral square error (ISE), integral absolute error (IAE) and mean absolute error (MAE) were employed to investigate the performances of the proposed controllers. Based on the comparative analysis, the upright stabilization of the pendulum was achieved within the shortest possible time with both controllers however, the LMI controller exhibits better performances in both stabilization and robustness. Moreover, the LMI control scheme is effective and simple.
This work treats the modeling and simulation of non-linear system behavior of an induction motor using backstepping sliding mode control (BACK- SMC). First, the direct field oriented control IM is derived. Then, a sliding for direct field oriented control is proposed to compensate the uncertainties, which occur in the control. Finally, the study of Backstepping sliding controls strategy of the induction motor drive. Our non linear system is simulated in MATLAB SIMULINK environment, the results obtained illustrate the efficiency of the proposed control with no overshoot, and the rising time is improved with good disturbances rejections comparing with the classical control law.
Stabilized controller of a two wheels robotjournalBEEI
The Segway Human Transport (HT) robot, it is dynamical self-balancing robot type. The stability control is an important thing for the Segway robot. It is an indisputable fact that Segway robot is a natural instability framework robot. The case study of the Segway robot focuses on running balance control systems. The roll, pitch, and yaw balance of this robot are obtained by estimating the Kalman Filter with a combination of the pole placement and the Linear Quadratic Regulator (LQR) control method. In our system configuration, the mathematical model of the robot will be proved by Matlab Simulink by modelling of the stabilizing control system of all state variable input. Furthermore, the implementation of this system modelled to the real-time test of the Segway robot. The expected result is by substitute the known parameters from Gyro, Accelero and both rotary encoder to initial stabilize control function, the system will respond to the zero input curve. The coordinate units of displacement response and inclination response pictures are the same. As our expected, the response of the system can reach the zero point position.
Modelling and Control of Ground Test Set-up of Attitude of SatelliteA. Bilal Özcan
In this study, the first simulation study of a laboratory product, in which attitude control of the nanosatellite will be made with 4 reaction wheels and inertia sensor placed on the nanosatellite prototype, different controllers will be designed by the user and tested in the simulation environment, and the simulation results will be verified with the experiments on the real system.
Within the scope of the simulation study, the dynamic and kinematic equations of the system, the motor’s mathematical model, and the mixer system that gives the necessary voltage to the motors are modeled. The nonlinear satellite ground test system was controlled in 3-axes utilizing the PID controller according to the criteria selected in the time domain, and the attitude control of the nonlinear system was achieved.
Link: https://www.researchgate.net/publication/354385104_Modelling_and_Control_of_Ground_Test_Set-up_of_Attitude_of_Satellite_Uydu_Yonelimi_Yer_Test_Duzeneginin_Modellemesi_ve_Kontrolu
Intermittent predictive control of an inverted pendulumIvan Tim Oloya
The Rotary Inverted Pendulum (RIP) represents a broad class of under actuated sys-tems making its control a classic problem. The dynamic equations used to represent the RIP are complex and nonlinear, which makes design and control of the system challenging.
This paper presents swing up and stabilization of the RIP system. The controller used to achieve swing up is energy based and acts by adding energy to the system until the pendulum reaches the linear region in the vertical upright position. A high gain ob-server has been implemented to estimate the unmeasurable system states during swing up.
Once in the linear region, a stabilizing control is switched on. The switch on is made possible by designing a mode-switching strategy to determine the point at which tran-sition occurs. A linearized model of the RIP is used to determine the feedback gains needed for stabilization by applying the Linear-Quadratic Regulator (LQR) method.
Two different stabilizing controllers are compared. An intermittent controller, which can be either time-triggered or event-triggered and a continuous predictive controller. A linear Luenberger observer has been designed to estimate the unmeasurable system states when the pendulum has switched to stabilization control.
Effectiveness of this system has been verified through simulation using MATLAB and Simulink.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document summarizes a research paper on designing a composite robotic controller using reduced order observer, output feedback, and LQR control methods.
[1] It develops an efficient method to linearize the dynamic model of a two-link robot manipulator around a linear trajectory using Lagrangian mechanics and LQR control. [2] The controller uses state feedback, output feedback, and a reduced order observer to estimate immeasurable states and make the system sensorless. [3] Computer simulations show the proposed controller provides good trajectory tracking and improved state estimation for the two-link manipulator.
Real Time Implementation of Fuzzy Adaptive PI-sliding Mode Controller for Ind...IJECEIAES
In this work, a fuzzy adaptive PI-sliding mode control is proposed for Induction Motor speed control. First, an adaptive PI-sliding mode controller with a proportional plus integral equivalent control action is investigated, in which a simple adaptive algorithm is utilized for generalized soft-switching parameters. The proposed control design uses a fuzzy inference system to overcome the drawbacks of the sliding mode control in terms of high control gains and chattering to form a fuzzy sliding mode controller. The proposed controller has implemented for a 1.5kW three-Phase IM are completely carried out using a dSPACE DS1104 digital signal processor based real-time data acquisition control system, and MATLAB/Simulink environment. Digital experimental results show that the proposed controller can not only attenuate the chattering extent of the adaptive PI-sliding mode controller but can provide high-performance dynamic characteristics with regard to plant external load disturbance and reference variations.
Optimal and pid controller for controlling camera’s position in unmanned aeri...Zac Darcy
This paper describes two controllers designed specifically for adjusting camera’s position in a small unmanned aerial vehicle (UAV). The optimal controller was designed and first simulated by using MATLAB technique and the results displayed graphically, also PID controller was designedand simulatedby using MATLAB technique .The goal of this paper is to connect the tow controllers in cascade mode to obtain the desired performance and correction in camera’s position in both roll and pitch.
Optimal and Pid Controller for Controlling Camera's Position InUnmanned Aeria...Zac Darcy
This paper describes two controllers designed specifically for adjusting camera’s position in a small
unmanned aerial vehicle (UAV). The optimal controller was designed and first simulated by using
MATLAB technique and the results displayed graphically, also PID controller was designedand
simulatedby using MATLAB technique .The goal of this paper is to connect the tow controllers in cascade
mode to obtain the desired performance and correction in camera’s position in both roll and pitch.
Modeling, simulation and control of a robotic armcesarportilla8
This document presents the modeling, simulation, and control of a robotic arm system using a DC motor. It describes the mathematical modeling of the DC motor and robotic arm dynamics. An open-loop simulation is performed to analyze stability and response. A closed-loop system is then designed using a PID controller. Different controller parameters are tested to meet design specifications of less than 5% overshoot, settling time less than 2 seconds, and zero steady-state error. Both P and PID controllers are able to achieve the specifications, with the PID controller providing faster response.
This document summarizes a research paper on using fuzzy logic to control the speed of a DC motor. It begins by describing the objectives of designing a high-current driver circuit for the motor and using an efficient fuzzy logic algorithm to accurately track motor velocity. It then provides mathematical analysis of the separately excited DC motor and describes implementing a fuzzy logic controller using FPGA. Membership functions, rule inference, and defuzzification are implemented in VHDL. Feedback is provided by an optical encoder to measure motor speed. Simulation results show the fuzzy controller can accurately control motor speed. In conclusion, the fuzzy logic approach provides an efficient and accurate method for DC motor speed control that does not require an accurate mathematical model of the motor.
This document proposes a model-free controller to control the positioning of an antenna's azimuth without needing a mathematical model of the system. The model-free controller has a simple structure with a derivative controller as the in-loop part and a lag compensator as the out-loop part. It requires only one tuning parameter and has a simple tuning rule to drive the error to zero. Simulations show the model-free controller can achieve comparable performance to advanced controllers like PID, FLC, LQR, and QFT but with less design time and effort since it does not require modeling the system.
IRJET- Speed Control of Induction Motor using Hybrid PID Fuzzy ControllerIRJET Journal
This document presents a study on using a hybrid PID fuzzy controller with a BAT optimization algorithm to control the speed of an induction motor. It begins with background on PID controllers and fuzzy logic controllers. It then proposes using a BAT algorithm to select the Kp and Ki parameters of a PI controller to regulate motor speed. The results show that the proposed BAT-PID controller reduces speed fluctuations and settling time compared to a traditional PID controller. In conclusion, the hybrid fuzzy-PID controller with BAT optimization improves induction motor speed control.
This document describes the modeling and control of a helicopter (CE 150) system connected to a computer. It includes:
1) An overview of the helicopter hardware, software environments, and its two degrees of freedom (elevation and azimuth).
2) The development of nonlinear and linear mathematical models from balancing forces and moments. System parameters are identified.
3) Details on the hardware (I/O cards) and software used to control the helicopter from a computer in real-time, including MATLAB and Simulink.
4) The design of PID and state feedback controllers using pole placement to control the helicopter dynamics.
1) The document describes modeling a DC motor system using Simulink and modeling the mechanical and electrical portions. It also involves producing translational motion and observing outputs with different spring rates.
2) PID control of the DC motor system is explored through proportional, PI, PD, and full PID control. Effects of changing gains on response are examined. PID tuning in Simulink is demonstrated to automatically determine gains.
3) Tuning the PID controller for the DC motor model in Simulink is shown, with the controller sampled at 0.02 seconds. Gains are automatically determined through PID tuning to achieve reasonable performance and robustness based on the linearized plant model. Output response is observed and commenting on speed
This document discusses various types of motor control, including on-off control and PID control. It begins with an overview of closed-loop control using motor feedback via encoders for velocity and position control. The main focus is on introducing PID control in a step-wise manner, first explaining on-off control and then proportional, integral and derivative controllers. It provides the mathematical formulas for these controller types and discusses implementing them in software and tuning the PID parameters.
Lecture Notes: EEEC4340318 Instrumentation and Control Systems - Introductio...AIMST University
(1) The document discusses control systems and provides examples of various control system applications. It introduces open and closed loop control systems and how they differ.
(2) Block diagrams are presented for several control system examples, including temperature control of an electric furnace, speed control of a turntable, and disk drive read system control.
(3) Exercises and problems are also included, asking the reader to draw block diagrams for control systems like laser power control, automated highway merging, air conditioning control, and aircraft collision avoidance.
Design of imc based controller for industrial purpose375ankit
The document presents an overview of a dissertation preliminary presentation on the robustness characteristics of controllers and IMC-based controllers. It discusses topics like the effect of uncertainty, robust control toolbox algorithms, robustness analysis of controllers, internal model control, IMC-based controller design for delay-free and time-delay processes, tuning IMC-based PID controllers, and comparing the performance of traditional controllers to IMC-based controllers. Examples are provided to illustrate IMC-based controller design and tuning for first-order and second-order systems. Simulation results show IMC controllers achieve better rise time, settling time and overshoot compared to auto-tuned controllers.
The document discusses control systems for robot manipulators. It covers open-loop and closed-loop control systems, with closed-loop being preferred using feedback. It describes using linear control techniques to approximate manipulator dynamics and designing controllers to meet stability and performance specifications. Common control techniques for manipulators are also summarized like PD, PID, state space control and adaptive/intelligent methods.
This document presents a second order integral sliding mode control approach for speed control of a DC motor system. The DC motor system is modeled as a second order system with uncertainty and disturbances. Three controllers are designed and compared - a second order integral sliding mode controller, a conventional sliding mode controller, and a PID controller. Simulation results show that the proposed second order integral sliding mode controller has the fastest response time, no overshoot, smooth control input, and drives the sliding surface to zero, performing better than the other controllers in terms of robustness and disturbance rejection.
The document describes modeling and control of aircraft pitch dynamics. It presents the longitudinal equations of motion, transfer function and state-space models. PID and root locus controllers are designed in MATLAB to meet requirements like overshoot <10% and settling time <10 seconds. A Simulink model is built containing the physical setup, state-space model and open/closed loop responses. PID control achieves the design goals while root locus analysis places poles for natural frequency >0.9 rad/sec and zero steady-state error.
The document discusses electric power system operation and control. It addresses the objectives of power system operation which are to provide continuous quality service to energy users at minimum cost. This includes supplying power at acceptable voltage and frequency while minimizing environmental impact and ensuring security and reliability. The tasks of operation planning, control and accounting are described. Operation planning involves scheduling generation and transmission facilities to meet load demand at minimum cost over various time periods. Operation control functions like economic dispatch, load frequency control and operating reserve calculation aim to satisfy instantaneous load demands. Optimization of generation dispatch to minimize total operating costs is formulated as a constrained optimization problem solved using methods like Lagrange multipliers and iterative techniques. Transmission losses are also accounted for in the optimal load dispatch model
Reviews of Cascade Control of Dc Motor with Advance Controllerijsrd.com
The proportional- integral-derivative (PID) control is the most used algorithm to regulate the armature current and speed of cascade Control system in motor drives. The controller uses two PID controllers. One PI controller is for speed control and second PID controller for current control in cascade structure. Inner loop is for the current control which is faster than the outer loop. Outer loop is for speed control. The output of the encoder is compared with a preset reference speed. The output of the PI controller is summed and is given as the input to the current controller.
Speed control of dc motor using relay feedback tuned piAlexander Decker
This document discusses speed control of a DC motor using different controller types, including a relay feedback tuned PI controller, fuzzy PI controller (FPIC), and self-tuned fuzzy PI controller (STFPIC). The FPIC and STFPIC are developed using fuzzy logic to overcome limitations of conventional PID controllers for nonlinear systems without an accurate mathematical model. An experimental setup is used to test the controllers' performance on a DC motor. Results show the model-independent STFPIC and FPIC controllers improve speed control performance compared to the relay-tuned PI controller.
This document describes the design of a PI controller to minimize speed error for a DC servo motor. It presents a mathematical model of the DC servo motor and designs a PI controller using Simulink. The PI controller gains are adjusted to minimize overshoot, rise time, settling time, and speed error when the reference input changes between 110V to 220V and 110V to 55V. Simulation results show the PI controller is effective at maintaining near zero speed error and improving transient response.
Iaetsd design of fuzzy self-tuned load frequency controller for power systemIaetsd Iaetsd
This document describes a self-tuning fuzzy controller designed for load frequency control (LFC) in a multi-machine power system. Conventional PID gains are first obtained using ant colony system optimization. These gains are then used to design fuzzy controller gains to solve the LFC problem under different loading conditions and non-linearities like generation rate constraints. The proposed self-tuning fuzzy controller is tested on a practical thermal and hydel power system and shown to perform better than conventional integral and ACS-PID controllers in dealing with system uncertainties and changing operating conditions.
This document describes a study comparing different speed control methods for a separately excited DC motor using MATLAB simulation. It develops a mathematical model of the DC motor and designs proportional-integral-derivative (PID), internal model control (IMC), and fuzzy logic controllers. It then simulates the performance of each controller and analyzes the step response results. The fuzzy logic controller provided the fastest rise time and lowest overshoot compared to the PID and IMC controllers.
Similar to Real-time PID control of an inverted pendulum (20)
Teleoperating a robotic arm through a gyroscopic helmetFrancesco Corucci
I worked on this project for a Real Time Systems class. It is basically a pointing device, based on a gyroscopic helmet controlling a little robotic arm. The hardware consists of Evidence FLEX boards running a Real-Time OS called Erika Enterprise (Open Source RTOS for single- and multi-core applications)
This is the final report for a Social Network Analysis class, within the context of the Excellence Program in Computer Engineering, University of Pisa, Italy
A wearable MIDI interface using a wireless sensor networkFrancesco Corucci
The idea behind this project is to use a wearable WSN to produce music. The user wears a certain number of sensors, sampling accelerometric data: these data are mapped into MIDI messages, that are routed toward a musical software. The WSN is seen as a standard input MIDI peripheral.
The platform allows new expressive forms based on gestuality: using more sensors placed on the body (with an appropriate mapping), it could be possible to link different forms of expressivity, such dance and music.
Implementation of a lane-tracking system for autonomous driving using Kalman ...Francesco Corucci
This project was developed for a Digital Control class. It consists of a system that is able to identify and track lane marks in a video acquired by webcam. It's interesting how the Kalman filter is used in such a context in order to make the lane detection computationally feasible in the small amount of time between two subsequent video frames
An overview on Quantum Key Distribution, final presentation for a Quantum Computing Class within the Excellence Program in Computer Engineering, University of Pisa, Italy
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Monitoring Java Application Security with JDK Tools and JFR Events
Real-time PID control of an inverted pendulum
1. 1
Real-time PID control of an
inverted pendulum
Pasquale Buonocunto –
Francesco Corucci –
MSc in Computer Engineering, University of Pisa – Italy
"In theory, there is no difference between theory and practice.
But, in practice, there is"
Jan L. A. van de Snepscheut
1 Introduction
In this report we will describe a project we developed for an Embedded
Systems class. Our goal was to experiment in practice with a simple control
plant, that could be approached with the famous PID controller.
When moving from simulation to practical implementation a lot of
unexpected issues arise, and must be handled: this forced us to deeply
understand the essence of the problem.
2 Brief description of the physical system
The case study consisted in the control of an inverted pendulum, in the
cart-and-pole version. As shown in Figure 1, there is a pole with a mass
mounted on his top, hinged on a cart that can move horizontally. The
control goal is to stabilize the pole in the vertical position ( ),
corresponding to an unstable equilibrium.
Figure 1 - Model of the physical system
2. 2
3 Testbed
3.1 Plant
Figure 2 shows the real plant on which we worked. The cart is actuated
with a DC motor through a belt: the motor also provides an encoder, that
we used to deduce the horizontal position of the cart. The inclination of the
pole is measured with a potentiometer placed at its bottom.
Figure 2 – The plant
The motor (Figure 3) is a ESCAP DC
MOTOR/ENCODER 22V E9-0500-2.0-1: it
is a little bit underutilized since the board
Belt
Potentiometer
DC Motor
Motor encoder
Figure 3 - The DC motor used to control
the plant
3. 3
provides it less voltage/current than expected, but we used it anyway since
we had just demonstration purposes (otherwise a dedicated power circuit
should have been necessary). Other limitations of the plant will be
discussed later in this paper.
3.2 Control electronics
Figure 4 shows the control electronics.
Figure 4 - Control electronics
We used an Evidence1
FLEX motion board equipped with the DC
Motor plug-in. The board mounts a microcontroller of the dsPIC33 serie.
We also realized a simple circuit with three potentiometers, in order to be
able to regulate online the control parameters. The wiring is realized as
follows (refer to the appendix for the nomenclature):
1
http://www.evidence.eu.com
CON23
PIN NO. 4 3 2 1
CONNECTED TO B A + -
CON22
PIN NO. 2
CONNECTED TO ENC_STDBY
CON24
PIN NO. 4 3
CONNECTED TO Vmotor+ Vmotor-
4. 4
CON8
PIN NO. 20 19 12 10 2 1
CONNECTED
TO
- + Ki_POT Kd_POT POLE_POT Kp_POT
DC Motor + encoder
B, A, +, -
Vmotor+, Vmotor-
ENC_STDBY
EncoderDC motor
Kp Ki Kd
+
Bar
potentiometer
-
Figure 5 - Wiring scheme
5. 5
4 Angle control: the first PID controller
For what concerns the stabilization of the pole, we implemented a speed
control of the DC motor using a PID controller.
Below the digital implementation of the PID controller (simplified code):
float PID(float setpoint,float actual_position)
{
static float pre_error = 0.0;
static float integral = 0.0;
static EE_UINT8 count = 0;
static float errors[I_WINDOW_SIZE];
float error;
float derivative;
float output;
EE_UINT8 i;
...
// Calculate error
error = setpoint - actual_position;
// Integral component
if(Ki > 0.0 && WINDOWED_INTEGR)
{ // Windowed integration mode
integral = 0.0;
// The truncated integration is realized with
// a cyclic array
// Insert new value in the window
errors[count] = error;
// Update counter
count = (count+1) % I_WINDOW_SIZE;
// Accumulation
for(i = 0; i < I_WINDOW_SIZE; i++)
integral += errors[i]*dt;
}
else if(Ki > 0.0) // Non-windowed integration mode
integral += error*dt;
//
// Anti-windup saturation of integral component
//
float Ki_integral = Ki*integral;
if(Ki_integral> ANTI_WINDUP_THR)
Ki_integral = ANTI_WINDUP_THR;
6. 6
else if(Ki_integral < -ANTI_WINDUP_THR)
Ki_integral = -ANTI_WINDUP_THR;
// Derivative component
derivative = (error - pre_error)/dt;
// Calculate output
output = Kp*error + Ki_integral + Kd*derivative;
...
//Saturation Filter
if(output > MAX_PID)
{
output = MAX_PID;
}
else if(output < MIN_PID)
{
output = MIN_PID;
}
//Update error
pre_error = error;
return output;
}
Figure 6 - Digital implementation of the PID controller
Some comments about the code:
- It is possible to choose two different modes of integration: windowed
and not windowed
- Our PID provides an anti-windup saturation of the integral component
- The control output is (obviously) saturated
5 Position control: the second PID
A PID is sufficient to stabilize the pole in a vertical position, but since
the rail is bounded it is important to make the controller aware of the fact
that it could end up hitting the physical bounds of the rail (this is possible
in practice using an encoder placed on the DC motor, whose angular
information is translated into a linear one). A software strategy that tries
to manually manage this condition preserving the stability of the pole is
quite difficult to think. We thought that the better thing to do was to try
to keep the cart away from the bounds (ideally in the center of the rail)
7. 7
with some additional control, in order to decrease the probability of hitting
the bounds.
Theoretically speaking it should be necessary a much more complex
modellization in order to simultaneously control the angle of the pole and
the position of the cart, since the two variables are not independent.
However, trying to keep things easy, we tried an alternative way to realize
something similar to the ideal behavior. We programmed two independent
PID controllers, one for the angle stabilization, one for the position
stabilization, and we tuned the parameters in order to give more priority to
the angle stabilization: this way we are able to obtain a quite stable system,
that tries to converge to the center of the rail when this does not disturb
the pole stabilization. This corresponds to a simplification of the model,
that can be mitigated with an appropriate tuning of the parameters2
. We
like to think this simplified modellization as if the second PID (the position
control) acts like a disturbance on the first PID (the angle stabilization
controller). Figure 7 shows the block description of the system.
The code below illustrates how this was implemented (pseudocode):
float current_position = <get pole inclination>;
float current_ticks = <get cart displacement from center>;
float angle_control = PID1(ANGLE_SETPOINT,
current_position);
float position_control = PID2(POSITION_SETPOINT,
current_ticks);
float control = angle_control + position_control;
<saturation of the resulting control variable>
Figure 8 - Control variable calculation
2
This is the interpretation we gave to our idea, that seems to be confirmed by
some works found in literature, like [1]
Ref
angle
PID1
PID2
Ref
pos
+ PLANT
+
-
+
-
pole_angle
cart_position
angle_control
pos_control
control
Figure 7 - Complete control system
8. 8
For what concerns the output control variable (controlling the speed of the
DC motor), it was saturated from -100 to +100, using the sign as
discriminator for the direction. The amplitude is from 0 to 100 because the
control is a PWM duty creating a linear voltage for the DC motor.
However, due to practical limitations caused by the cart friction on the rail,
the minimum PWM duty cycle usefull to really move the cart is quite high
(about 30% with good lubrification), so we limitated it in a shorter range
defined by two thresholds (±[MIN_PID, MAX_PID]).
6 PID tuning
Given the control system architecture, the proper way to tune the whole
system is the following:
1. Keeping the second PID disabled, tune the parameters of the first
PID (the angle stabilization one) as if it is the only controller in the
system;
2. Once the angle stability is reached, enable and tune the second PID
(the position control one) with “light” parameters, in order to preserve
the angle stabilization previously obtained.
The single PID can be tuned with any of the classical methods, such
Ziegler-Nichols: we followed an heuristic methodology inspired to the latter,
also driven by the practical interpretation we gave to every parameter.
7 Software organization
The application is composed of 5 tasks:
- TASK_HOMING [aperiodic]: activated only one time at the startup, it
manages the necessary homing steps in order to make system aware of
the control set points (rail center, and pole equilibrium position). It
requires the user intervention.
- TASK_POSITION_CONTROL [period: 150ms, priority: 53
]: reads the
position of the cart and calculates the PID for the position control. The
computed value is used from the the angle control task, that performs
the real actuation merging the two control variables.
- TASK_ANGLE_CONTROL [period: 50ms, priority: 6]: reads the
inclination of the bar, calculates the PID for the stabilization of the
bar, combines this value with the one from the position control task,
then actuates the motor.
3
Task priority in OSEK is specified by a number from 1 to 10. Higher values
represents higher priorities
9. 9
- TASK_READ_POT [period: 200ms, priority: 3]: this task acquires
values from the three external potentiometer, and updates the control
parameters accordingly.
- TASK_SERIAL_PRINT [period: 500ms, priority: 2]: feedsback some
useful information via UART.
Figure 9 shows the execution flow of the application:
8 Running demonstration
A running demonstration of the implemented system can be viewed in the
following videos:
http://www.youtube.com/watch?v=zEF6a_m0kdQ
http://www.youtube.com/watch?v=9Mg-y6TDef4
9 Limitations
The physical system we worked on presented considerable non-idealities,
that made quite hard the task of making things work properly. The main
limitation of the plant relies in the friction between the rail and the cart,
that remains considerable also with appropriate lubrification. Also the
traction offered by the belt is asymmetric in the two direction and causes
some extra friction. Another issue relies in the motor, its poor precision and
its underutilization (since the board provides it less voltage/current than
1. Maintain the bar, press the
button, and let the cart
scan the rail to identify the
center
2. Once the cart have reached
the center of the rail, put
the pole in the steady state
and press again the button
main init
TASK_HOMING
button pressed / activate task
TASK_POSITION_
CONTROL
TASK_ANGLE_
CONTROL
button pressed / activate task
TASK_SERIAL_
PRINT
TASK_READ_
POT
Figure 9 - Execution flow
10. 10
expected). The final effect of these limitation is that the movements of the
cart are inevitably jerky, making quite difficult to obtain a smooth control
and, as a consequence, a good stabilization of the system.
10 Future work
The potentiometer used to read the inclination of the pole is quite noisy:
with a rough strategy, we just truncated the measures after the second
decimal digit, because all we got beyond that was nothing but noise. This
was enough for our purposes, but it should be interesting, instead, to
implement some filtering strategy to gain accuracy in the angle
measurement. For example it could be usefull to oversample the value with
a higher frequency in respect to the control frequency (i.e. the frequency
with which the measure is consumed), and then apply some filtering (es:
low pass, median, …). Another filter that is commonly used in this context
is the Kalman filter, used to estimate the measure ensuring a good
immunity to noise.
11 References
[1] “Simulation and Robustness Studies on an Inverted Pendulum”, HUANG
Chun-E, LI Dong-Hai, SU Yong, Proceedings of the 30th Chinese Control
Conference, July 22-24, 2011, Yantai, China