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.

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.

Backstepping Adaptive Iterative Learning Control for Robotic Systems

2013 ◽  
Vol 284-287 ◽  
pp. 1759-1763
Author(s):  
Ying Chung Wang ◽  
Chiang Ju Chien ◽  
Chi Nan Chuang
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Fuzzy Neural Network ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Robotic Systems ◽  
Adaptive Iterative Learning Control ◽  
Repetitive Tasks ◽  
Fuzzy Neural

A backstepping adaptive iterative learning control for robotic systems with repetitive tasks is proposed in this paper. The backstepping-like procedure is introduced to design the AILC. A fuzzy neural network is applied for compensation of the unknown certainty equivalent controller. Using a Lyapunov like analysis, we show that the adjustable parameters and internal signals remain bounded, the tracking error will asymptotically converge to zero as iteration goes to infinity.

ADAPTIVE ITERATIVE LEARNING CONTROL OF ROBOTIC SYSTEMS USING BACKSTEPPING DESIGN

2013 ◽  
Vol 37 (3) ◽  
pp. 591-601 ◽  
Author(s):  
Ying-Chung Wang ◽  
Chiang-Ju Chien ◽  
Chi-Nan Chuang
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Certainty Equivalent ◽  
Robotic Systems ◽  
Backstepping Design ◽  
Adaptive Iterative Learning Control ◽  
Repetitive Tasks

In this paper, a backstepping adaptive iterative learning control (AILC) is proposed for robotic systems with repetitive tasks. The AILC is designed to approximate unknown certainty equivalent controller. Finally, we apply a Lyapunov like analysis to show that all adjustable parameters and the internal signals remain bounded for all iterations.

Adaptive Iterative Learning Control for Robot Manipulators with Initial Resetting Errors

2011 ◽  
Vol 130-134 ◽  
pp. 265-269 ◽  
Author(s):  
Jian Ming Wei ◽  
Yun An Hu
Keyword(s):  
Iterative Learning Control ◽  
Robot Manipulators ◽  
Error Function ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Unknown Parameters ◽  
Adaptive Iterative Learning Control ◽  
Suitable Parameter ◽  
Parameter Values

In this paper, an adaptive iterative learning control is presented for robot manipulators with unknown parameters, performing repetitive tasks. In order to overcome the initial resetting errors, an auxiliary tracking error function is introduced. The adaptive algorithm is derived along the iteration axis to search for suitable parameter values. The technical analysis shows convergence of the tracking errors. Finally, simulation results are provided to illustrate the effectiveness of the proposed controller.

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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