Design and Experiment of Iterative Learning Controller Based on Particle Swarm Optimization Approach with New Bounded Constraints Technique

2013 ◽  
Vol 284-287 ◽  
pp. 2233-2237 ◽  
Author(s):  
Yi Cheng Huang ◽  
Yi Hao Li ◽  
Shu Ting Li
Keyword(s):  
Particle Swarm Optimization ◽  
Iterative Learning Control ◽  
Pid Controller ◽  
Particle Swarm ◽  
Iterative Learning ◽  
Optimization Approach ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Nonminimum Phase ◽  
Motion Profile

This paper utilizes the Improved Particle Swarm Optimization (IPSO) with bounded constraints technique for adjusting the gains of a Proportional-Integral-Derivative (PID) and Iterative Learning Control (ILC) controllers. This study compares the conventional ILC-PID controller with proposed IPSO-ILC-PID controller. A cycloid trajectory for mimicking the real industrial motion profile is applied. Two system plants with nonminimum phase are numerically simulated. Proposed IPSO with bounded constraints technique is evaluated on one axis of linear synchronous motor (LSM) with a PC-based real time controller. Simulations and experiment results show that the proposed controller can reduce the error significantly after two iterations.

Optimum Setting of DC Drives PID Controller via Particle Swarm Optimization

2012 ◽  
Vol 157-158 ◽  
pp. 88-93 ◽  
Author(s):  
Guang Hui Chang ◽  
Jie Chang Wu ◽  
Chao Jie Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Position Control ◽  
Particle Swarm ◽  
Dc Motor ◽  
Motor Drive ◽  
Proportional Integral Derivative ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Simulation Results

In this paper, an intelligent controller of PM DC Motor drive is designed using particle swarm optimization (PSO) method for tuning the optimal proportional-integral-derivative (PID) controller parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency.To show the validity of the PID-PSO controller, a DC motor position control case is considered and some simulation results are shown. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment.. It can be easily seen from the simulation results that the proposed method will have better performance than those presented in other studies.

scholarly journals Optimal Learning Controller Design Using Particle Swarm Optimization: Applied to CSI System

2020 ◽  
Vol 16 (1) ◽  
pp. 104-112
Author(s):  
Khulood Omran ◽  
Abdul-Basset Al-Hussein ◽  
Basil Jassim
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Learning Control ◽  
Iterative Learning ◽  
Constant Current ◽  
Switching Frequency ◽  
Optimization Approach ◽  
Output Current ◽  
Optimal Learning ◽  
Swarm Optimization

In this article, a PD-type iterative learning control algorithm (ILC) is proposed to a nonlinear time-varying system for cases of measurement disturbances and the initial state errors. The proposed control approach uses a simple structure and has an easy implementation. The iterative learning controller was utilized to control a constant current source inverter (CSI) with pulse width modulation (PWM); subsequently the output current trajectory converged the sinusoidal reference signal and provided constant switching frequency. The learning controller's parameters were tuned using particle swarm optimization approach to get best optimal control for the system output. The tracking error limit is achieved using the convergence exploration. The proposed learning control scheme was robust against the error in initial conditions and disturbances which outcome from the system modeling inaccuracies and uncertainties. It could correct the distortion of the inverter output current waveform with less computation and less complexity. The proposed algorithm was proved mathematically and through computer simulation. The proposed optimal learning method demonstrated good performances.

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