This paper illustrates a Comparative study of highly non-linear, complex and multivariable Inverted
Pendulum (IP) system on Cart using different soft computing techniques. Firstly, a Fuzzy logic controller
was designed using triangular and trapezoidal shape Membership functions (MF's). The trapezoidal fuzzy
controller shows better results in comparison to triangular fuzzy controller. Secondly, an Adaptive neuro
fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy
controller. Finally, the study illustrates the effect of variation in shape of MF's on Performance parameters
of the IP system. The results shows that ANFIS controller provides better results in comparison to both
fuzzy controller.
Simulation design of trajectory planning robot manipulatorjournalBEEI
Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.
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.
This paper is focused on developing a platform that
helps researchers to create verify and implement their
machine learning algorithms to a humanoid robot in real
environment. The presented platform is durable, easy to fix,
upgrade, fast to assemble and cheap. Also, using this platform
we present an approach that solves a humanoid balancing
problem, which uses only fully connected neural network as a
basic idea for real time balancing. The method consists of 3
main conditions: 1) using different types of sensors detect the
current position of the body and generate the input
information for the neural network, 2) using fully connected
neural network produce the correct output, 3) using servomotors make movements that will change the current position
to the new one. During field test the humanoid robot can
balance on the moving platform that tilts up to 10 degrees to
any direction. Finally, we have shown that using our platform
we can do research and compare different neural networks in
similar conditions which can be important for the researchers
to do analyses in machine learning and robotics.
Three phase induction motor Induction is one of the widest spread motor due to its
robustness, simple construction, no need for complex circuits for starting. With several
available speed control techniques, this paper presents a new Proportional-Integral (PI)
controller and Artificial Neural Network (ANNs) control system based on vector control
scheme. MATLAB/SIMULINK software may be used to create a 3phase induction engine
model. To achieve the effectiveness of the controller, the system is subjected to external
disturbance. Experimental results are presented and satisfied with the controller results.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Simulation design of trajectory planning robot manipulatorjournalBEEI
Robots can be mathematically modeled with computer programs where the results can be displayed visually, so it can be used to determine the input, gain, attenuate and error parameters of the control system. In addition to the robot motion control system, to achieve the target points should need a research to get the best trajectory, so the movement of robots can be more efficient. One method that can be used to get the best path is the SOM (Self Organizing Maps) neural network. This research proposes the usage of SOM in combination with PID and Fuzzy-PD control for finding an optimal path between source and destination. SOM Neural network process is able to guide the robot manipulator through the target points. The results presented emphasize that a satisfactory trajectory tracking precision and stability could be achieved using SOM Neural networking combination with PID and Fuzzy-PD controller.The obtained average error to reach the target point when using Fuzzy-PD=2.225% and when using PID=1.965%.
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.
This paper is focused on developing a platform that
helps researchers to create verify and implement their
machine learning algorithms to a humanoid robot in real
environment. The presented platform is durable, easy to fix,
upgrade, fast to assemble and cheap. Also, using this platform
we present an approach that solves a humanoid balancing
problem, which uses only fully connected neural network as a
basic idea for real time balancing. The method consists of 3
main conditions: 1) using different types of sensors detect the
current position of the body and generate the input
information for the neural network, 2) using fully connected
neural network produce the correct output, 3) using servomotors make movements that will change the current position
to the new one. During field test the humanoid robot can
balance on the moving platform that tilts up to 10 degrees to
any direction. Finally, we have shown that using our platform
we can do research and compare different neural networks in
similar conditions which can be important for the researchers
to do analyses in machine learning and robotics.
Three phase induction motor Induction is one of the widest spread motor due to its
robustness, simple construction, no need for complex circuits for starting. With several
available speed control techniques, this paper presents a new Proportional-Integral (PI)
controller and Artificial Neural Network (ANNs) control system based on vector control
scheme. MATLAB/SIMULINK software may be used to create a 3phase induction engine
model. To achieve the effectiveness of the controller, the system is subjected to external
disturbance. Experimental results are presented and satisfied with the controller results.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
—This paper presents a new image based visual servoing (IBVS) control scheme for omnidirectional wheeled mobile robots with four swedish wheels. The contribution is the proposal of a scheme that consider the overall dynamic of the system; this means, we put together mechanical and electrical dynamics. The actuators are direct current (DC) motors, which imply that the system input signals are armature voltage applied to DC motors. In our control scheme the PD control law and eye-to-hand camera configuration are used to compute the armature voltages and to measure system states, respectively. Stability proof is performed via Lypunov direct method and LaSalle's invariance principle. Simulation and experimental results were performed in order to validate the theoretical proposal and to show the good performance of the posture errors. Keywords—IBVS, posture control, omnidirectional wheeled mobile robot, dynamic actuator, Lyapunov direct method.
Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System IJECEIAES
The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The nonlinear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
As the technology is fast changing, there is more and more use of machine intelligence in modern motor controllers. These controllers are employed in advanced electric motor drives in particular, the present day Induction motor drives. These systems emulate the human logic. This is particularly useful when the application has poorly defined mathematical model. In this present paper the analysis of fuzzy logic as the artificial intelligence is used. The comparative study of Fuzzy PI, Fuzzy MRAC is made. There is always a compromise of the cost and complexity. So this paper presents a new approach and its dynamic response in comparison to the Fuzzy PI and Fuzzy MRAC. The proposed controller is Fuzzy PI with scaling factors. This approach is validated with the Speed, torque responses of Indirect vector controlled Induction motor (IVCIM) drive.
Fuzzy gain scheduling control apply to an RC Hovercraft IJECEIAES
The Fuzzy Gain Scheduling (FGS) methodology for tuning the ProportionalIntegral-Derivative (PID) traditional controller parameters by scheduling controlled gains in different phases, is a simple and effective application both in industries and real-time complex models while assuring the high achievements over pass decades, is proposed in this article. The Fuzzy logic rules of the triangular membership functions are exploited on-line to verify the Gain Scheduling of the Proportional-Integral-Derivative controller gains in different stages because it can minimize the tracking control error and utilize the Integral of Time Absolute Error (ITAE) minima criterion of the controller design process. For that reason, the controller design could tune the system model in the whole operation time to display the efficiency in tracking error. It is then implemented in a novel Remote Controlled (RC) Hovercraft motion models to demonstrate better control performance in comparison with the PID conventional controller.
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.
Automatic car parking mechanism using neuro fuzzy controller tuned by genetic...SumitDutta58
This paper has being presented a neuro fuzzy controller
based on Gaussian type RBF neural network, where all the
parameters can be simultaneously tuned by GA. By
appropriate coding of NFLC parameters it can achieve self
tuning properties from an initial random state.
Development of a quadruped mobile robot and its movement system using geometr...journalBEEI
As the main testbed platform of Artificial Intelligence, the robot plays an essential role in creating an environment for industrial revolution 4.0. According to their bases, the robot can be categorized into a fixed based robot and a mobile robot. Current robotics research direction is interesting since people strive to create a mobile robot able to move in the land, water, and air. This paper presents development of a quadruped mobile robot and its movement system using geometric-based inverse kinematics. The study is related to the movement of a four-legged (quadruped) mobile robot with three Degrees of Freedom (3 DOF) for each leg. Because it has four legs, the movement of the robot can only be done through coordinating the movements of each leg. In this study, the trot gait pattern method is proposed to coordinate the movement of the robot's legs. The end-effector position of each leg is generated by a simple trajectory generator with half rectified sine wave pattern. Furthermore, to move each robot's leg, it is proposed to use geometric-based inverse kinematic. The experimental results showed that the proposed method succeeded in moving the mobile robot with precision. Movement errors in the translation direction are 1.83% with the average pose error of 1.33 degrees, means the mobile robot has good walking stability.
Co-simulation of self-adjusting fuzzy PI controller for the robot with two-ax...TELKOMNIKA JOURNAL
This paper presents the co-simulation of the self-adjusting fuzzy PI controller to control a two-axes system. Each axis was driven by a permanent magnet linear synchronous motor (PMLSM). The position and speed controller used the fuzzy PI algorithm with parameters adjusted by a radial basis function neural network (RBFNN). The vector control was applied to the decoupled effect of the PMLSM. The field programmable gate array (FPGA) was used to control both axes of the system. The very high-speed integrated circuit-hardware description language (VHDL) was developed in the Quartus II software environment, provided by Altera, to analyze and synthesize designs. Firstly, the mathematical model of PMLSM and fuzzy PI was introduced. Secondly, the RBFNN adjusted the knowledge base of the fuzzy PI. Thirdly, the motion trajectory was introduced for testing the control algorithm. Fourthly, the implementation of the controller based on FPGA with the FSM method and the structure of co-simulation between Matlab/Simulink and ModelSim were set up. Finally, discussion about the results proved the effectiveness of the control system, determining the exact position and trajectory of the XY axis system. This research was successful in implementing a two-motor controller within one chip.
Case Study of Various Parameters by Applying Swing Up Control for Inverted Pe...ijeei-iaes
This paper investigates behavior of the system in terms of time response e.g., steady state error, rise time & overshoot & then compare it with FLC. Being an unstable system, Inverted Pendulum is very common control problem being assigned to control its dynamics. It is almost impossible to balance a pendulum in the inverted position except applying some force from outside to the system.
Fractional-order sliding mode controller for the two-link robot arm IJECEIAES
This study presents a control system of the two-link robot arm based on the sliding mode controller with the fractional-order. Firstly, the equations of the two-link robot arm are analyzed, then the author proposes the controller for each joint based on these equations. The controller is a sliding mode controller with its order is not an integer value. The task of the control system is controlling the torques acted on the joints so that the response angle of each link equal to the desired angle. The effectiveness of the proposed control system is demonstrated through Matlab-Simulink software. The robot model and controller are built for investigating the efficiency of the system. The result shows that the system quality is very good: there is not the chattering phenomenon of torques, the response angle of two links always follow the desired angle with the short transaction time and the static error of zero.
Vibration and tip deflection control of a single link flexible manipulatorijics
In this paper, a hybrid control scheme for vibration and tip deflection control of a single link flexible
manipulator system is presented. The purpose of this control is for input tracking, vibration control of hub
angle and tip deflection control. The control scheme consists of a resonant controller and a fuzzy logic
controller (FLC).The resonant controller is used as the inner loop feedback controller for vibration control
using the resonant frequencies at different resonant modes of the system which were determined from
experiment. The fuzzy logic controller is designed as the outer loop feedback controller for the tracking
control and to achieve zero steady state error. The performance of the proposed control scheme is
investigated via simulations and the results show the effectiveness of the control scheme, in addition the
controller is tested to show it robustness using different values of payload.
A Comparative study of controllers for stabilizing a Rotary Inverted Pendulumijccmsjournal
This paper describes comparative study of various controllers on Rotary Inverted Pendulum (RIP). PID,
LQR, FUZZY LOGIC and H∞ controllers are tried on RIP in MatLab Simulink. The same four controllers
have been tested on test bed of RIP system the controllers are compared from various aspects. The
controllers in simulink are compared with the controllers in real time.
Identification and Control of Three-Links Electrically Driven Robot Arm Using...Waqas Tariq
This paper uses a fuzzy neural network (FNN) structure for identifying and controlling nonlinear dynamic systems such three links robot arm. The equation of motion for three links robot arm derived using Lagrange’s equation. This equation then combined with the equations of motion for dc. servo motors which actuated the robot. For the control problem, we present the forward and inverse adaptive control approaches using the FNN. Computer simulation is performed to view the results for identification and control
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
—This paper presents a new image based visual servoing (IBVS) control scheme for omnidirectional wheeled mobile robots with four swedish wheels. The contribution is the proposal of a scheme that consider the overall dynamic of the system; this means, we put together mechanical and electrical dynamics. The actuators are direct current (DC) motors, which imply that the system input signals are armature voltage applied to DC motors. In our control scheme the PD control law and eye-to-hand camera configuration are used to compute the armature voltages and to measure system states, respectively. Stability proof is performed via Lypunov direct method and LaSalle's invariance principle. Simulation and experimental results were performed in order to validate the theoretical proposal and to show the good performance of the posture errors. Keywords—IBVS, posture control, omnidirectional wheeled mobile robot, dynamic actuator, Lyapunov direct method.
Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System IJECEIAES
The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The nonlinear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.
Comparison of different controllers for the improvement of Dynamic response o...IJERA Editor
As the technology is fast changing, there is more and more use of machine intelligence in modern motor controllers. These controllers are employed in advanced electric motor drives in particular, the present day Induction motor drives. These systems emulate the human logic. This is particularly useful when the application has poorly defined mathematical model. In this present paper the analysis of fuzzy logic as the artificial intelligence is used. The comparative study of Fuzzy PI, Fuzzy MRAC is made. There is always a compromise of the cost and complexity. So this paper presents a new approach and its dynamic response in comparison to the Fuzzy PI and Fuzzy MRAC. The proposed controller is Fuzzy PI with scaling factors. This approach is validated with the Speed, torque responses of Indirect vector controlled Induction motor (IVCIM) drive.
Fuzzy gain scheduling control apply to an RC Hovercraft IJECEIAES
The Fuzzy Gain Scheduling (FGS) methodology for tuning the ProportionalIntegral-Derivative (PID) traditional controller parameters by scheduling controlled gains in different phases, is a simple and effective application both in industries and real-time complex models while assuring the high achievements over pass decades, is proposed in this article. The Fuzzy logic rules of the triangular membership functions are exploited on-line to verify the Gain Scheduling of the Proportional-Integral-Derivative controller gains in different stages because it can minimize the tracking control error and utilize the Integral of Time Absolute Error (ITAE) minima criterion of the controller design process. For that reason, the controller design could tune the system model in the whole operation time to display the efficiency in tracking error. It is then implemented in a novel Remote Controlled (RC) Hovercraft motion models to demonstrate better control performance in comparison with the PID conventional controller.
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.
Automatic car parking mechanism using neuro fuzzy controller tuned by genetic...SumitDutta58
This paper has being presented a neuro fuzzy controller
based on Gaussian type RBF neural network, where all the
parameters can be simultaneously tuned by GA. By
appropriate coding of NFLC parameters it can achieve self
tuning properties from an initial random state.
Development of a quadruped mobile robot and its movement system using geometr...journalBEEI
As the main testbed platform of Artificial Intelligence, the robot plays an essential role in creating an environment for industrial revolution 4.0. According to their bases, the robot can be categorized into a fixed based robot and a mobile robot. Current robotics research direction is interesting since people strive to create a mobile robot able to move in the land, water, and air. This paper presents development of a quadruped mobile robot and its movement system using geometric-based inverse kinematics. The study is related to the movement of a four-legged (quadruped) mobile robot with three Degrees of Freedom (3 DOF) for each leg. Because it has four legs, the movement of the robot can only be done through coordinating the movements of each leg. In this study, the trot gait pattern method is proposed to coordinate the movement of the robot's legs. The end-effector position of each leg is generated by a simple trajectory generator with half rectified sine wave pattern. Furthermore, to move each robot's leg, it is proposed to use geometric-based inverse kinematic. The experimental results showed that the proposed method succeeded in moving the mobile robot with precision. Movement errors in the translation direction are 1.83% with the average pose error of 1.33 degrees, means the mobile robot has good walking stability.
Co-simulation of self-adjusting fuzzy PI controller for the robot with two-ax...TELKOMNIKA JOURNAL
This paper presents the co-simulation of the self-adjusting fuzzy PI controller to control a two-axes system. Each axis was driven by a permanent magnet linear synchronous motor (PMLSM). The position and speed controller used the fuzzy PI algorithm with parameters adjusted by a radial basis function neural network (RBFNN). The vector control was applied to the decoupled effect of the PMLSM. The field programmable gate array (FPGA) was used to control both axes of the system. The very high-speed integrated circuit-hardware description language (VHDL) was developed in the Quartus II software environment, provided by Altera, to analyze and synthesize designs. Firstly, the mathematical model of PMLSM and fuzzy PI was introduced. Secondly, the RBFNN adjusted the knowledge base of the fuzzy PI. Thirdly, the motion trajectory was introduced for testing the control algorithm. Fourthly, the implementation of the controller based on FPGA with the FSM method and the structure of co-simulation between Matlab/Simulink and ModelSim were set up. Finally, discussion about the results proved the effectiveness of the control system, determining the exact position and trajectory of the XY axis system. This research was successful in implementing a two-motor controller within one chip.
Case Study of Various Parameters by Applying Swing Up Control for Inverted Pe...ijeei-iaes
This paper investigates behavior of the system in terms of time response e.g., steady state error, rise time & overshoot & then compare it with FLC. Being an unstable system, Inverted Pendulum is very common control problem being assigned to control its dynamics. It is almost impossible to balance a pendulum in the inverted position except applying some force from outside to the system.
Fractional-order sliding mode controller for the two-link robot arm IJECEIAES
This study presents a control system of the two-link robot arm based on the sliding mode controller with the fractional-order. Firstly, the equations of the two-link robot arm are analyzed, then the author proposes the controller for each joint based on these equations. The controller is a sliding mode controller with its order is not an integer value. The task of the control system is controlling the torques acted on the joints so that the response angle of each link equal to the desired angle. The effectiveness of the proposed control system is demonstrated through Matlab-Simulink software. The robot model and controller are built for investigating the efficiency of the system. The result shows that the system quality is very good: there is not the chattering phenomenon of torques, the response angle of two links always follow the desired angle with the short transaction time and the static error of zero.
Vibration and tip deflection control of a single link flexible manipulatorijics
In this paper, a hybrid control scheme for vibration and tip deflection control of a single link flexible
manipulator system is presented. The purpose of this control is for input tracking, vibration control of hub
angle and tip deflection control. The control scheme consists of a resonant controller and a fuzzy logic
controller (FLC).The resonant controller is used as the inner loop feedback controller for vibration control
using the resonant frequencies at different resonant modes of the system which were determined from
experiment. The fuzzy logic controller is designed as the outer loop feedback controller for the tracking
control and to achieve zero steady state error. The performance of the proposed control scheme is
investigated via simulations and the results show the effectiveness of the control scheme, in addition the
controller is tested to show it robustness using different values of payload.
A Comparative study of controllers for stabilizing a Rotary Inverted Pendulumijccmsjournal
This paper describes comparative study of various controllers on Rotary Inverted Pendulum (RIP). PID,
LQR, FUZZY LOGIC and H∞ controllers are tried on RIP in MatLab Simulink. The same four controllers
have been tested on test bed of RIP system the controllers are compared from various aspects. The
controllers in simulink are compared with the controllers in real time.
Identification and Control of Three-Links Electrically Driven Robot Arm Using...Waqas Tariq
This paper uses a fuzzy neural network (FNN) structure for identifying and controlling nonlinear dynamic systems such three links robot arm. The equation of motion for three links robot arm derived using Lagrange’s equation. This equation then combined with the equations of motion for dc. servo motors which actuated the robot. For the control problem, we present the forward and inverse adaptive control approaches using the FNN. Computer simulation is performed to view the results for identification and control
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Abstract - This paper addresses some of the potential benefits of
using fuzzy logic controllers to control an inverted pendulum
system. The stages of the development of a fuzzy logic controller
using a four input Takagi-Sugeno fuzzy model were presented.
The main idea of this paper is to implement and optimize fuzzy
logic control algorithms in order to balance the inverted
pendulum and at the same time reducing the computational time
of the controller. In this work, the inverted pendulum system was
modeled and constructed using Simulink and the performance of
the proposed fuzzy logic controller is compared to the more
commonly used PID controller through simulations using Matlab.
Simulation results show that the Fuzzy Logic Controllers are far
more superior compared to PID controllers in terms of overshoot,
settling time and response to parameter changes.
This paper describes comparative study of various controllers on Rotary Inverted Pendulum (RIP). PID,
LQR, FUZZY LOGIC and H∞ controllers are tried on RIP in MatLab Simulink. The same four controllers
have been tested on test bed of RIP system the controllers are compared from various aspects. The
controllers in simulink are compared with the controllers in real time.
To design and implementation of variable and constant with no load for induction motor (IM) that is the goal in this work. This paper was including three parts, first the simulation model with no load for IM, Second the simulation model with constant load for IM, Third the simulation model with variable load for IM. In addition, this work includes comparative between two different controllers (PI and fuzzy logic control (FLC). The simulation results clearly the implementation of variable and constant with no load for IM. The simulation response of the system achieves better results when choosing to use type fuzzy-PI controller technique comparison with conventional PI controller and improve the performance of the system at different operation conditions.
As the robot manipulators are highly nonlinear, time varying and Multiple Input Multiple Output (MIMO)
systems, one of the most important challenges in the field of robotics is robot manipulators control with
acceptable performance. In this research paper, a simple and computationally efficient Fuzzy Logic
Controller is designed based on the Fuzzy Lyapunov Synthesis (FLS) for the position control of PUMA-560
robot manipulator. The proposed methodology enables the designer to systematically derive the rule base
thereby guarantees the stability of the controller. The methodology is model free and does not require any
information about the system nonlinearities, uncertainties, time varying parameters, etc. The performance
of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. The closed-loop control
performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a
large set of rules requires more on-line computational time and more parameters need to be adjusted.
Here, a Fuzzy Logic Controller is first designed and then the controller based on FLS is designed and
simulated with a minimum rule base. Finally the simulation results of the proposed controller are
compared with that of the normal Fuzzy Logic Controller and PD controlled Computed Torque Controller
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A COMPARATIVE ANALYSIS OF FUZZY BASED HYBRID ANFIS CONTROLLER FOR STABILIZATION AND CONTROL OF NONLINEAR SYSTEMS
1. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
DOI: 10.14810/ijscmc.2015.4402 13
A COMPARATIVE ANALYSIS OF FUZZY
BASED HYBRID ANFIS CONTROLLER FOR
STABILIZATION AND CONTROL OF NON-
LINEAR SYSTEMS
Ashwani Kharola
PhD Scholar, Department of Mechanical Engineering, Graphic Era University, Dehradun
Senior Research Fellow (SRF), Institute of Technology Management (ITM)
Defence Research & Development Organization (DRDO), Landour Cantt Mussoorie
ABSTRACT
This paper illustrates a Comparative study of highly non-linear, complex and multivariable Inverted
Pendulum (IP) system on Cart using different soft computing techniques. Firstly, a Fuzzy logic controller
was designed using triangular and trapezoidal shape Membership functions (MF's). The trapezoidal fuzzy
controller shows better results in comparison to triangular fuzzy controller. Secondly, an Adaptive neuro
fuzzy inference system (ANFIS) controller was used to optimize the results obtained from trapezoidal fuzzy
controller. Finally, the study illustrates the effect of variation in shape of MF's on Performance parameters
of the IP system. The results shows that ANFIS controller provides better results in comparison to both
fuzzy controller.
KEYWORDS
Inverted Pendulum, Fuzzy logic, ANFIS, Performance parameters, Matlab-Simulink.
1. INTRODUCTION TO INVERTED PENDULUM
Inverted Pendulum(IP) systems belongs to a class of highly non-linear and complex systems
which act as testing bed for many unstable systems[1]. The control and stabilization of IP at
upright position is one of the most challenging problem in the field of control engineering since
1950s[7,8]. IP finds many applications in following control techniques including feedback
stabilization[2], friction compensation[3], hybrid control[4], back stepping control[5] etc.
Figure 1.0 Inverted Pendulum on Cart
2. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
14
A view of IP on Cart is shown in Fig 1.0.[6]. The Cart is driven by electric motor while a
pendulum freely pivoted to it. The pendulum is highly unstable and falls in case of any
disturbance. The IP system has two equilibrium; one is stable while the other is unstable. In case
of the stable equilibrium position, the pendulum is pointing downwards. In absence of any control
force, the system will be in this state. In unstable equilibrium pendulum points strictly upwards
and, thus, requires a control force to maintain this position. The control objective of this study is
to maintain the unstable equilibrium position when the pendulum initially starts in an upright
position [15]. The dynamics of inverted pendulum is quite similar to two-wheeled mobile robots
[9,10], flexible link robot [11], biped robot limbs [12,13], missiles [14] etc.
2. DERIVATION OF GOVERNING MATHEMATICAL EQUATIONS
FOR IP SYSTEM
The IP system consists of a pendulum of mass, m, pivoted to a cart of mass, M. The pendulum is
inclined at an angle, θ from vertical axis as shown in figure 1.1. A force, F is required to push the
cart horizontally, the coefficient of friction, b, length of pendulum L and the second moment of
inertia for pendulum, I [16].
Figure 1.1 Diagram of Inverted Pendulum
For modelling and simulation of IP system the following dynamic equations are derived using
Free body diagrams (FBD) of cart and pendulum sub-systems [17]. A view of FBD of cart and
pendulum are shown in figure 1.2 and figure 1.3 respectively.
Figure 1.2 FBD of Cart Sub-system Figure 1.3 FBD of Pendulum Sub-system
3. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
15
The various parameters of IP system considered for this study are shown in the table 1.0.[23]
Parameter Magnitude
Mass of Cart (M) 1.0kg
Mass of Pendulum (m) 0.5kg
Length of Pendulum (L) 1.0m
Moment of inertia of Pendulum (I) 0.006kg
Acceleration due to gravity (g) 9.8m/
Coefficient of friction (b) 0.1N/m/sec
Table 1. Parameters of IP systems
The equations of motion are derived separately for cart and pendulum sub-systems
Equations derived for Cart:
M = ∑F (1)
F= M + b + N (2)
= ( − − ) (3)
Where, N and P are the interaction forces between the cart and pendulum.
Equations derived for Pendulum:
I = ∑τ (4)
=
(NLcosθ + PLsinθ) (5)
The interaction forces N and P should also be considered in order to model complete dynamics of
the system. Therefore modelling of x and y dynamics of the pendulum is done in addition to its
theta dynamics. The additional x and y equations for the pendulum are given below:
m = ∑F=N (6)
m = P - mg (7)
P = m( + ) (8)
However and are exact functions of theta. Therefore, their derivatives are represented in
terms of the derivatives of theta
4. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
16
= x-Lsinθ (9)
= -L cosθ (10)
= + L sinθ -L cosθ (11)
= Lcosθ (12)
= -L sinθ (13)
= - L cosθ - L sinθ (14)
After substituting these equations into eq. 6 and 8, we get:
N = m( - L cosθ + L sinθ (15)
P = m(-L cosθ - L sinθ) (16)
The eq. 15 and eq. 16 were used for developing, a Matlab Simulink model of IP system [24].
3.IP SYSTEM CONTROL USING FUZZY INFERENCE
CONTROLLER
In this study two different fuzzy logic controllers(FLC) [24] were used to control the IP system.
The ‘FLC-1’ receives Cart position and Cart velocity as inputs while Force is the output. The
inputs for 'FLC-2' were Pendulum angle and Angular velocity while Force is the output. The net
Force is obtained by summing the forces obtained from the both the controllers which is further
used as input to Simulink of IP sub-system.
3.1 Designing of Membership function's (MF's) for Fuzzy logic Controllers
This study considers nine linguistic variables namely Negative Extreme(NE), Negative Big(NB),
Negative Medium(NM), Negative Small(NS), Zero(ZE), Positive Small(PS), Positive
Medium(PM), Positive Big(PB) and Positive Extreme(PE) for designing MF's [25] of both the
controllers. A view of MF's are shown in figure 1.4 and figure 1.5.
5. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
17
Figure 1.4 MF’s of input variable ‘Cart Position’ for FLC-1
Figure 1.5 MF’s of output variable ‘Force’ for FLC-2
3.2 Designing of fuzzy control rules
The fuzzy control rules for both the fuzzy controllers were designed using the experience of experts
and varies from one expert to another. A view of fuzzy control rules for FLC-1 is shown in table
1.1.
Table 1.1 Fuzzy control rules for FLC-1
Fuzzy logic toolbox allows the user to represent if-then fuzzy rules in 3-D format with the help of a
Surface viewer. A 3-D view of surface viewer for FLC-1 is shown in figure 1.6
Figure 1.6 Surface Viewer for FLC-1
6. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
18
4.BUILDING SIMULINK OF IP SYSTEM
The Simulink of IP was build in two stages. Initially a Simulink model of IP sub-system was built
which was finally masked to give the complete system. A view of IP system and its sub-system
are shown in figure 1.7 and figure 1.8 respectively.
Figure 1.7 Simulink model of IP system Figure 1.8 Simulink model of IP Sub-
system
5.DESIGNING OF ANFIS CONTROLLER FOR IP
ANFIS belongs to a class of adaptive networks that combines the features of both fuzzy inference
system and neural networks. ANFIS uses the learning ability of neural networks and linguistic
application of fuzzy logic [26]. It performs an input output mapping based on both human
knowledge (fuzzy if then rules) and on generated input output data pairs [18]. ANFIS uses a
Takagi-Sugeno inference system[19], in which output of each rule can be a linear combination of
input variables plus a constant term or can be only a constant term. The final output is the
weighted average of each rule’s output. ANFIS employs Back propagation[27] or hybrid[28]
learning algorithm to optimize input data sets[20,21].
5.1 ANFIS control of IP
This study has considered two different data sets to train cart and pendulum controllers in ANFIS.
A total of 713 data sets were collected which were further divided into training and testing data
sets. The pre-designed fuzzy controller was used to generate data sets. Initially the training data
was loaded from the workspace to ANFIS Toolbox and then Grid Partition method [29] was used
to generate the initial FIS structure in ANFIS. A view of loading of training data sets and initial
FIS generated is shown in Figure 1.9 and Figure 2.0 respectively.
7. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
19
Figure 1.9 loaded training data in ANFIS
Figure 2.0 Initial FIS generated for Cart controller
The ANFIS controller uses a Takagi-Sugeno [30] inference system of 16 fuzzy if-then rules and 4
MF's. The shape of the MF's considered for input and output variables were generalized bell
shape and linear respectively. A view of initial MF's before training and final MF's obtained after
training are shown in figure 2.1 and figure 2.2.
Figure 2.1 MF’s before training for ‘Cart Position’
Figure 2.2 MF’s after training for ‘Cart Position’
8. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
20
In this study a Hybrid method[22] is used to train the ANFIS controller. Hybrid method is
basically a combination of Back-propagation and least square method. The error tolerance and
number of epochs were set to 0 and 665 respectively. The error obtained after training is
0.003826. A view of training in ANFIS is shown in figure 2.3
Figure 2.3 Training ANFIS controller
6. SIMULATION RESULTS AND COMPARISON
The Modelling and Simulation of IP system was done in Matlab-Simulink environment. A view
of simulation results are illustrated from figure 2.4 to figure 2.7.
Fig 2.4 Output response of Cart Position using Fuzzy controller
S.No Performance Parameter Output response
1 Settling Time (sec) 18 sec
2 Steady state error 0
3 Maximum Overshoot (degree) 1.42º
Table 1.2 Output response of Cart Position using Fuzzy controller
Fig 2.5 Output response of Pendulums Angular velocity using Fuzzy controller
9. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
21
S.No Performance Parameter Output response
1 Settling Time (sec) 21.5 sec
2 Steady state error 0
3 Maximum Overshoot (degree) 0.5º
Table 1.3 Output response of Pendulums Angular velocity using Fuzzy controller
Fig 2.6 Output response for Cart Position using ANFIS Controller
S.No Performance Parameter Output response
1 Settling Time (sec) 15 sec
2 Steady state error 0
3 Maximum Overshoot (degree) 8.8º
Table 1.4 Output response for Cart Position using ANFIS Controller
Fig 2.7 Output response of Pendulums Angular velocity using ANFIS controller
S.No Performance Parameter Output response
1 Settling Time (sec) 16 sec
2 Steady state error 0
3 Maximum Overshoot (degree) 1.7º
Table 1.5 Output response of Pendulums Angular velocity using ANFIS controller
10. International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015
22
7. CONCLUSION & FUTURE RECOMMENDATIONS
The Research objective of the study to show a comparative analysis between Fuzzy and ANFIS
control of highly non-linear IP system has been achieved. The results proves the validity of
proposed techniques. The results shows that the ANFIS controller provides better results as
compared to Fuzzy controller. The Setting time for Cart position and Pendulum angle is reduced
by 3 sec and 5.5 sec respectively using ANFIS controllers. It is also observed from the results that
the value of maximum overshoot for both Cart and Pendulum using ANFIS controller is very
large as compared to that of Fuzzy controller. Both the controllers shows an excellent response
towards steady state error. The results obtained can be further refined to optimize the
performance parameters specially settling time for both the controllers. Also the maximum
overshoot of ANFIS controllers can be further reduced by varying values of certain attributes like
training and testing data sets, MF's, training algorithm etc. As an extent to future work more Soft-
computing techniques like Neural networks, Particle Swarm Optimization, Genetic Algorithm etc
can also be implemented for the control of non-linear systems.
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Authors
Ashwani Kharola received B.Tech (Honours) in Mechanical Engineering from
Dehradun Institute of Technology, Dehradun in 2010 and M.Tech in CAD/CAM &
Robotics from Graphic Era University, Dehradun in 2013. He obtained Silver Medal in
M.Tech for (2011-13) batch. Currently he is working as Senior Research Fellow (SRF)
in Institute of Technology Management (ITM), One of premier training institute of
Defence Research & Development Organisation (DRDO), Ministry of Defence, Govt.
of India. He is also pursuing PhD in Mechanical Engineering from Graphic Era University. He has
published more than 16 National/International papers in peer reviewed ISSN Journals and IEEE
Conferences. His current areas of work includes Fuzzy logic reasoning, Adaptive Neuro-fuzzy inference
system (ANFIS) control, Neural Networks, Mathematical Modeling & Simulation, etc.