Precision Motion of Iterative Learning Controller Using Adaptive Filter Bandwidth Tuning by Improved Particle Swarm Optimization Technique

2013 ◽  
Vol 376 ◽  
pp. 349-353
Author(s):  
Yi Cheng Huang ◽  
Shu Ting Li ◽  
Kuan Heng Peng

This paper utilized the Improved Particle Swarm Optimization (IPSO) technique for adjusting the gains of PID and the bandwidth of zero-phase Butterworth Filter of an Iterative Learning Controller (ILC) for precision motion. Simulation results show that IPSO-ILC-PID controller without adaptive bandwidth filter tuning have the chance of producing high frequencies in the error signals when the filter bandwidth is fixed for every repetition. However the learnable and unlearnable error signals should be separated for bettering control process. Thus the adaptive bandwidth of a zero phase filter in ILC-PID controller with IPSO tuning is applied to one single motion axis of a CNC table machine. Simulation results show that the developed controller can cancel the errors efficiently as repetition goes. The frequency response of the error signals is analyzed by the empirical mode decomposition (EMD) and the Hilbert-Huang Transform (HHT) method. Errors are reduced and validated by ILC with adaptive bandwidth filtering design.

Author(s):  
Mahdieh Adeli ◽  
Hassan Zarabadipoor

In this paper, anti-synchronization of discrete chaotic system based on optimization algorithms are investigated. Different controllers have been used for anti-synchronization of two identical discrete chaotic systems. A proportional-integral-derivative (PID) control is used and its parameters is tuned by the four optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), modified particle swarm optimization (MPSO) and improved particle swarm optimization (IPSO). Simulation results of these optimization methods to determine the PID controller parameters to anti-synchronization of two chaotic systems are compared. Numerical results show that the improved particle swarm optimization has the best result.


2012 ◽  
Vol 157-158 ◽  
pp. 88-93 ◽  
Author(s):  
Guang Hui Chang ◽  
Jie Chang Wu ◽  
Chao Jie Zhang

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.


2011 ◽  
Vol 130-134 ◽  
pp. 1938-1942
Author(s):  
Xia Bo Shi ◽  
Wei Xing Lin

This paper presents a new approach of PID parameter optimization for the induction motor speed system by using an improved particle swarm optimization (IPSO). The induction motor speed is changed by the stator voltage controlled with PID controller. The performance of PID controller based on IPSO is compared to Linearly Decreasing Inertia Weight (LIWPSO). Simulation results demonstrate that the IPSO algorithm has better dynamic performance, higher accuracy and faster convergence and good performance for the PID controller.


2013 ◽  
Vol 284-287 ◽  
pp. 2233-2237 ◽  
Author(s):  
Yi Cheng Huang ◽  
Yi Hao Li ◽  
Shu Ting Li

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.


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