scholarly journals High-Order Feedback Iterative Learning Control Algorithm with Forgetting Factor

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hongbin Wang ◽  
Jian Dong ◽  
Yueling Wang
Keyword(s):  
Nonlinear System ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
High Order ◽  
Forgetting Factor ◽  
Trajectory Simulation ◽  
Number Of Iterations

A novel iterative learning control (ILC) algorithm is proposed to produce output curves that pass close to the desired trajectory. The key advantage of the proposed algorithm is introducing forgetting factor, which is a function of the number of iterations. Due to the forgetting factor characteristic of ILC, the proposed scheme not only stabilizes the nonlinear system with uncertainties but also weakens interference on the tracking desired trajectory. Simulation examples are included to demonstrate feasibility and effectiveness of the proposed algorithm.

scholarly journals An Accelerating Iterative Learning Control Based on an Adjustable Learning Interval

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Dongqi Ma ◽  
Hui Lin
Keyword(s):  
Convergence Rate ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Law ◽  
Convergence Conditions ◽  
Monotonic Convergence ◽  
Number Of Iterations ◽  
Controlled Object

An iterative learning control algorithm with an adjustable interval is proposed for nonlinear systems to accelerate the convergence rate of iterative learning control. Forλ-norm, the monotonic convergence of ILC was analyzed, and the corresponding convergence conditions were obtained. The results showed that the convergence rate was mainly determined by the controlled object, the control law gain, the correction factor, and the iteration interval size and that the control law gain was corrected in real time in the modified interval and the modified interval shortened as the number of iterations increased, further accelerating the convergence. The numerical simulation shows the effectiveness of the proposed method.

scholarly journals Iterative Learning Control with Forgetting Factor for Linear Distributed Parameter Systems with Uncertainty

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xisheng Dai ◽  
Senping Tian ◽  
Wenguang Luo ◽  
Yajun Guo
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Matrix Theory ◽  
Learning Control ◽  
Distributed Parameter Systems ◽  
Iterative Learning ◽  
Distributed Parameter ◽  
Forgetting Factor ◽  
Forward Difference ◽  
The Matrix

Iterative learning control is an intelligent control algorithm which imitates human learning process. Based on this concept, this paper discussed iterative learning control problem for a class parabolic linear distributed parameter systems with uncertainty coefficients. Iterative learning control algorithm with forgetting factor is proposed and the conditions for convergence of algorithm are established. Combining the matrix theory with the basic theory of distributed parameter systems gives rigorous convergence proof of the algorithm. Finally, by using the forward difference scheme of partial differential equation to solve the problems, the simulation results are presented to illustrate the feasibility of the algorithm.

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.

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