Optimal Tuning of Fractional Order Proportional-Integral-Derivative Controller for Wire Feeder System Using Ant Colony Optimization

In this study, we deal with systems that can be represented by single fractional order pole models and propose an integer order proportional–integral/proportional–integral–derivative controller design methodology for this class. The basic principle or backbone of the design methodology of the proposed controller relies on using the inverse of the fractional model and then approximating this fractional controller transfer function by a low integer order model using Oustaloup filter. The emerging integer order controller reveals itself either in pre-filtered proportional–integral or proportional–integral–derivative form by emphasizing on the dominancy concept of pole-zero configuration. Parameters of the proposed controllers depend on the parameters of the single fractional order pole model and the only free design parameter left is the overall controller gain. This free design parameter is determined via some approximating functions relying on an optimization procedure. Simulation results show that the proposed controller exhibits either satisfactory or better results with respect to some performance indices and time domain criteria when they are compared to classical integer order proportional–integral–derivative and fractional order proportional–integral–derivative controllers. Moreover, the proposed controller is applied to real-time liquid level control system. The application results show that the proposed controller outperforms the other controllers.

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Multi‐objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques

Expert Systems ◽

10.1111/exsy.12366 ◽

2018 ◽

Vol 36 (2) ◽

pp. e12366 ◽

Cited By ~ 4

Author(s):

Thiagarajan Piraisoodi ◽

Willjuice Iruthayarajan Maria Siluvairaj ◽

Mohaideen Abdul Kadhar Kappuva

Keyword(s):

Evolutionary Computation ◽

Fractional Order ◽

Controller Design ◽

Hydraulic Turbine ◽

Proportional Integral Derivative ◽

Proportional Integral Derivative Controller ◽

Computation Techniques ◽

Proportional Integral ◽

Multi Objective ◽

Governing System

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Fractional order based trajectory tracking control of an ankle rehabilitation robot

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331216667810 ◽

2016 ◽

Vol 40 (2) ◽

pp. 550-564 ◽

Cited By ~ 10

Author(s):

Mustafa Sinasi Ayas ◽

Ismail Hakki Altas ◽

Erdinc Sahin

Keyword(s):

Fractional Order ◽

Trajectory Tracking ◽

Human Robot Interaction ◽

Rehabilitation Robot ◽

Proportional Integral Derivative ◽

External Disturbances ◽

Proportional Integral Derivative Controller ◽

Proportional Integral ◽

Rehabilitation Robots ◽

Ankle Rehabilitation

Human–robot interaction is inherently available and used actively in ankle rehabilitation robots. This interaction causes disturbances to be counteracted on the rehabilitation robots in order to reduce the side effects. This paper presents a fractional order proportional–integral–derivative controller to improve the trajectory tracking ability of a developed 2-degree of freedom parallel ankle rehabilitation robot subject to external disturbances. The parameters of the controller are optimally tuned by using both the cuckoo search algorithm and the particle swarm optimization algorithm. A traditional proportional–integral–derivative controller, which is also tuned using both of the algorithms, is designed to test the performance of the fractional order proportional–integral–derivative controller. The experimental results show that the optimally tuned FOPID controller improves the tracking performance of the ankle rehabilitation robot subject to external disturbances significantly and decreases the steady-state tracking errors compared to the optimally tuned PID controller.

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Tuning Fractional Order Proportional Integral Derivative Controller for DC Motor Control Model Using Cross-Entropy Method

2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE) ◽

10.1109/icitisee.2018.8721016 ◽

2018 ◽

Author(s):

Indra Kurniawan ◽

Adha Imam Cahyadi ◽

Igi Ardiyanto

Keyword(s):

Motor Control ◽

Fractional Order ◽

Control Model ◽

Entropy Method ◽

Cross Entropy ◽

Proportional Integral Derivative ◽

Proportional Integral Derivative Controller ◽

Proportional Integral ◽

Cross Entropy Method ◽

Dc Motor Control

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Hybrid control technique for minimizing the torque ripple of brushless direct current motor

Measurement and Control ◽

10.1177/0020294018786753 ◽

2018 ◽

Vol 51 (7-8) ◽

pp. 321-335 ◽

Cited By ~ 5

Author(s):

K Prathibanandhi ◽

R Ramesh

Keyword(s):

Fractional Order ◽

Direct Current ◽

Hybrid Technique ◽

Proportional Integral Derivative ◽

Inference System ◽

Proportional Integral Derivative Controller ◽

Proportional Integral ◽

Direct Current Motor ◽

Torque Ripples ◽

Brushless Direct Current Motor

This paper presented the brushless direct current motor torque ripple reduction based on the speed and torque control using hybrid technique. The dynamic behavior of the brushless direct current motor is analyzed in terms of the parameters such as the speed, current, back electromotive force and torque. Based on the parameters, the motor speed is controlled and minimized the torque ripples. For controlling the speed of the brushless direct current motor is utilized the fractional-order proportional–integral–derivative controller for generating the optimal control pulses. With the use of fractional-order proportional–integral–derivative controller, the optimal gain parameters are needed to reduce the torque ripples and control the speed of brushless direct current motor. By utilizing the hybrid technique, the gain parameters are utilized to analyze the optimal gain parameters of fractional-order proportional–integral–derivative controller. The hybrid technique is the combination of adaptive neuro-fuzzy inference system with firefly algorithm. The proposed strategy is simple in structure and robust to reduce the complexities of the mathematical computations. Initially, the nature inspired optimization algorithm of firefly algorithm is analyzed for finding the error function. In addition, the efficient adaptive neuro-fuzzy inference system controller which becomes an integrated method of approach is performed to control the error functions in order to yields excellent optimized gain values. After that, the control signals are applied to the input of voltage source converter of brushless direct current motor. With this control strategy, the harmonics and torque ripples are minimized. Based on the proposed control strategy, the speed and torque performance is analyzed. The effectiveness of the proposed technique is implemented in MATLAB/Simulink platform and evaluates their performance. The performance analysis of the proposed method is demonstrated and contrasted with the existing techniques such as bat algorithm, particle swarm optimization algorithm and ant–lion optimizer algorithm with fractional-order proportional–integral–derivative controller techniques.

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