Fuzzy PD iterative learning control algorithm for improving tracking accuracy

2002 ◽  
Author(s):  
Yang-Ming Pok ◽  
Kok-Hwa Liew ◽  
Jian-Xin Xu
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Tracking Accuracy ◽  
Fuzzy Pd

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

scholarly journals A New Linear Motor Force Ripple Compensation Method Based on Inverse Model Iterative Learning and Robust Disturbance Observer

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Xuewei Fu ◽  
Xiaofeng Yang ◽  
Zhenyu Chen
Keyword(s):  
Iterative Learning Control ◽  
Disturbance Observer ◽  
Learning Control ◽  
Inverse Model ◽  
Iterative Learning ◽  
Compensation Method ◽  
Tracking Accuracy ◽  
Linear Motors ◽  
Learning Speed ◽  
Force Ripple

Permanent magnet linear motors (PMLMs) are gaining increasing interest in ultra-precision and long stroke motion stage, such as reticle and wafer stage of scanner for semiconductor lithography. However, the performances of PMLM are greatly affected by inherent force ripple. A number of compensation methods have been studied to solve its influence to the system precision. However, aiming at some application, the system characteristics limit the design of controller. In this paper, a new compensation strategy based on the inverse model iterative learning control and robust disturbance observer is proposed to suppress the influence of force ripple. The proposed compensation method makes fully use of not only achievable high tracking accuracy of the inverse model iterative learning control but also the higher robustness and better iterative learning speed by using robust disturbance observer. Simulation and experiments verify effectiveness and superiority of the proposed method.

scholarly journals Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

2019 ◽  
Vol 292 ◽  
pp. 01010
Author(s):  
Mihailo Lazarević ◽  
Nikola Živković ◽  
Darko Radojević
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Closed Loop ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Control Law ◽  
Exact Linearization ◽  
Outer Loop ◽  
Control Scheme

The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.

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