scholarly journals Cascaded iterative learning control for improved task execution of optimal control

2003 ◽  
Author(s):  
A. Robertsson ◽  
D. Scalamogna ◽  
M. Grundelius ◽  
R. Johansson
Keyword(s):  
Optimal Control ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Task Execution

scholarly journals Higher-Order Iterative Learning Control with Optimal Control Gains Based on Evolutionary Algorithm for Nonlinear System

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yun-Shan Wei ◽  
Xiaofen Yang ◽  
Wenli Shang ◽  
Ying-Yu Chen
Keyword(s):  
Optimal Control ◽  
Evolutionary Algorithm ◽  
Iterative Learning Control ◽  
Fitness Function ◽  
Learning Control ◽  
Absolute Error ◽  
Higher Order ◽  
Iterative Learning ◽  
Discrete Time System ◽  
Maximum Absolute Error

For the nonlinear discrete-time system, higher-order iterative learning control (HOILC) with optimal control gains based on evolutionary algorithm (EA) is developed in this paper. Since the updating actions are constituted by the tracking information from several previous iterations, the suitably designed HOILC schemes with appropriate control gains usually achieve fast convergence speed. To optimize the control gains in HOILC approach, EA is introduced. The encoding strategy, population initialization, and fitness function in EA are designed according to the HOILC characteristics. With the global optimization of EA, the optimal control gains of HOILC are selected adaptively so that the number of convergence iteration is reduced in ILC process. It is shown in simulation that the sum absolute error, total square error, and maximum absolute error of tracking in the proposed HOILC based on EA are convergent faster than those in conventional HOILC.

scholarly journals Optimal Gains of Iterative Learning Control with Forgetting Factor

2019 ◽  
Vol 37 (5) ◽  
pp. 1077-1084
Author(s):  
Baolin Dai ◽  
Jun Gong ◽  
Cuiming Li
Keyword(s):  
Optimal Control ◽  
Iterative Learning Control ◽  
Matrix Theory ◽  
Optimization Problems ◽  
Learning Control ◽  
Convergence Condition ◽  
Iterative Learning ◽  
Convergence Speed ◽  
Forgetting Factor ◽  
Control Gain

In order to solve the optimization problems of convergence characteristics of a class of single-input single-output (SISO) discrete linear time-varying systems (LTI) with time-iteration-varying disturbances, an optimal control gain design method of PID type iterative learning control (ILC) algorithm with forgetting factor was presented. The necessary and sufficient condition for the ILC system convergence was obtained based on iterative matrix theory. The convergence of the learning algorithm was proved based on operator theory. According to optimization theory and Toeplitz matrix characteristics, the monotonic convergence condition of the system was established. The accurate solution of the optimal control gain and the relationship equation between the forgetting factor and the optimal control gains were obtained according to the optimal theory which ensures the fastest system convergence speed, thereby reaching the end of the system convergence improvement. The convergence condition is weaker than the known results. The proposed method overcomes the shortcomings of traditional optimal control gain in ILC algorithm with forgetting factor, effectively accelerates the system convergence speed, suppresses the system output track error fluctuation, and provides a better solution for LTI system with time-iteration-varying disturbances. Simulation verifies the effectiveness of the control algorithm.

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