An Optimal Fuzzy Self-Tuning PID Controller for Robot Manipulators via Genetic Algorithm

Meza, José Luis; Soto, Rogelio; Arriaga, Javier Trujillo

doi:10.1109/micai.2009.34

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…All of the controllers except the conventional PID controller are created by the Matlab/Simulink using embedded Matlab function. Inside the block, it contains the listing codes based-on the C language program in according to the gradient algorithm [6] and extended Kalman filter algorithm [17], respectively. In the case of the PID controller, we use the standard libraries in the Matlab.…”

Section: Experiments Setupmentioning

confidence: 99%

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A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System

Sento

Kitjaidure

2017

ECTI-CIT

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This paper presents a detailed study to demonstrate the online tuning dynamic neural network PID controller to improve a joint angle position output performance of 4-joint robotic arm. The proposed controller uses a new updating weight rule model of the neural network architecture using multi-loop calculation of the fusion of the gradient algorithm with the cubature Kalman filter (CKF) which can optimize the internal predicted state of the updated weights to improve the proposed controller performances, called a Hybrid CKF-NNPID controller. To evaluate the proposed controller performances, the demonstration by the Matlab simulation program is used to implement the proposed controller that connects to the 4joint robotic arm system. In the experimental result, it shows that the proposed controller is a superior control method comparing with the other prior controllers even though the system is under the loading criteria, the proposed controller still potentially tracks the error and gives the best performances.

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Section: Experiments Setupmentioning

confidence: 99%

“…They used the gradient algorithm to update the weight of the controller. In addition, there are other examples of the fusion of the computational intelligence with the conventional PID controller that were established such as [6], [7], and [8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System

Sento

Kitjaidure

2017

ECTI-CIT

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“…The third transfer function (Equation 5) is joined to the attempts of many researches of improving the tuning parameters through intelligent techniques (Meza et al, 2009):…”

Section: Tuning Parameters Of Pid Controllermentioning

confidence: 99%

Optimal Control Algorithms for Second Order Systems

Pelusi¹

2013

Journal of Computer Science

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Proportional Integral Derivative (PID) controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving timing performance of second order plants are proposed. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.

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“…The third transfer function choice (see equation ( 5)) is joined to the attempts of many researches of improving the tuning parameters through intelligent techniques [15] 2 400 ( ) 50 G s s s  …”

Section: Tuning Parameters Of Pid Controllermentioning

confidence: 99%

PID and Intelligent Controllers for optimal timing performances of industrial actuators

Pelusi

2012

International Journal of Simulation Systems Science &Amp; Technology

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PID controllers are widely used in process industries due to its simplicity and robustness. The main problem sometime is tuning the PID parameters in order to improve the settling time, the rise time and the overshoot. In literature, there are procedures to obtain the PID settings which gives the better performance and robustness. Some experiments on this research line show that the controller gain is only a function of the overshoot observed in the setpoint experiment. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Network. The first theory is good for decisional problems, the second one can be used in search algorithms and the Neural Networks have the capability to learn from data. The combination of these approaches can give good results in terms of settling time, rise time and overshoot. In this paper, we propose the design of suitable controllers which target is the improvement of timing performance of industrial actuators. The designed controllers are PID controller, genetic-fuzzy controller and neuro-fuzzy controller. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches.

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An Optimal Fuzzy Self-Tuning PID Controller for Robot Manipulators via Genetic Algorithm

Cited by 11 publications

References 12 publications

A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System

A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System

Optimal Control Algorithms for Second Order Systems

PID and Intelligent Controllers for optimal timing performances of industrial actuators

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