Adaptive Zero-Phase Filtering Bandwidth of Iterative Learning Control by Particle Swarm Optimization

2014 ◽  
pp. 1031-1037 ◽  
Author(s):  
Yi-Wei Su ◽  
Jen-Ai Chao ◽  
Yi-Cheng Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Iterative Learning Control ◽  
Particle Swarm ◽  
Learning Control ◽  
Iterative Learning ◽  
Swarm Optimization ◽  
Zero Phase

Iterative Learning Controller Based on Particle Swarm Optimization

2011 ◽  
Vol 403-408 ◽  
pp. 593-600
Author(s):  
Xiu Lan Wen ◽  
Hong Sheng Li ◽  
Dong Xia Wang ◽  
Jia Cai Huang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Particle Swarm ◽  
Learning Control ◽  
Iterative Learning ◽  
Improved Genetic Algorithm ◽  
Tracking Accuracy ◽  
Swarm Optimization

Iterative Learning Control (ILC) has recently emerged as a powerful control strategy that iteratively achieves a higher accuracy for systems with repetitive tasks. The basic idea of ILC is to construct a compensation signal based on the tracking error in each repetition so as to reduce the tracking error in the next repetition. In this paper, particle swarm optimization (PSO) is proposed to optimize the input of iterative learning controller. The experimental results confirm that the proposed method not only has higher tracking accuracy than that of Improved Genetic Algorithm (IGA) and traditional Genetic Algorithm based elisit strategy (EGA), but also has the advantages of simple algorithm and good flexibility. And compared with conventional iterative learning control methods, it is easy to solve the optimal input for non-linear plant models.

An Output Based Adaptive Iterative Learning Control with Particle Swarm Optimization for Robotic Systems

2013 ◽  
Vol 479-480 ◽  
pp. 737-741
Author(s):  
Ying Chung Wang ◽  
Chiang Ju Chien ◽  
Chi Nan Chuang
Keyword(s):  
Particle Swarm Optimization ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Particle Swarm ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Performance ◽  
Robotic Systems ◽  
Adaptive Iterative Learning Control ◽  
Swarm Optimization

We consider an output based adaptive iterative learning control (AILC) for robotic systems with repetitive tasks in this paper. Since the joint velocities are not measurable, a sliding window of measurements and an averaging filter approach are used to design the AILC. Besides, the particle swarm optimization (PSO) is used to adjust the learning gains in the learning process to improve the learning performance. Finally, a Lyapunov like analysis is applied to show that the norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.

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.

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.

Experiments of iterative learning control system using particle swarm optimization by new bounded constraints on velocity and positioning

2014 ◽  
Vol 31 (2) ◽  
pp. 250-266 ◽  
Author(s):  
Yi-Cheng Huang ◽  
Ying-Hao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Iterative Learning Control ◽  
Pid Controller ◽  
Learning Control ◽  
Iterative Learning ◽  
Experimental Results ◽  
Swarm Optimization ◽  
Content Type ◽  
Pid Algorithm

Purpose – This paper utilizes the improved particle swarm optimization (IPSO) with bounded constraints technique on velocity and positioning for adjusting the gains of a proportional-integral-derivative (PID) and iterative learning control (ILC) controllers. The purpose of this paper is to achieve precision motion through bettering control by this technique. Design/methodology/approach – Actual platform positioning must avoid the occurrence of a large control action signal, undesirable overshooting, and preventing out of the maximum position limit. Several in-house experiments observation, the PSO mechanism is sometimes out of the optimal solution in updating velocity and updating position of particles, the system may become unstable in real-time applications. The proposed IPSO with new bounded constraints technique shows a great ability to stabilize nonminimum phase and heavily oscillatory systems based on new bounded constraints on velocity and positioning in PSO algorithm is evaluated on one axis of linear synchronous motor with a PC-based real-time ILC. Findings – Simulations and experiment results show that the proposed controller can reduce the error significantly after two learning iterations. The developed method using bounded constraints technique provides valuable programming tools to practicing engineers. Originality/value – The proposed IPSO-ILC-PID controller overcomes the shortcomings of conventional ILC-PID controller with fixed gains. Simulation and experimental results show that the proposed IPSO-ILC-PID algorithm exhibits great speed convergence and robustness. Experimental results confirm that the proposed IPSO-ILC-PID algorithm is effective and achieves better control in real-time precision positioning.

Sign in / Sign up

Export Citation Format

Share Document