Iterative Learning Control for Linear Discrete-Time Systems with Unknown High-Order Internal Models: A Time-Frequency Analysis Approach

2017 ◽  
Vol 20 (1) ◽  
pp. 370-385
Author(s):  
Qiao Zhu ◽  
Jian-Xin Xu ◽  
Deqing Huang ◽  
Guang-Di Hu
Keyword(s):  
Discrete Time ◽  
Frequency Analysis ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
High Order ◽  
Analysis Approach ◽  
Time Frequency ◽  
Discrete Time Systems ◽  
Time Systems

scholarly journals Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-6
Author(s):  
Yun-Shan Wei ◽  
Qing-Yuan Xu
Keyword(s):  
Discrete Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Initial State ◽  
Tracking Errors ◽  
Discrete Time Systems ◽  
P Type ◽  
Time Systems

For linear discrete-time systems with randomly variable input trail length, a proportional- (P-) type iterative learning control (ILC) law is proposed. To tackle the randomly variable input trail length, a modified control input at the desirable trail length is introduced in the proposed ILC law. Under the assumption that the initial state fluctuates around the desired initial state with zero mean, the designed ILC scheme can drive the ILC tracking errors to zero at the desirable trail length in expectation sense. The designed ILC algorithm allows the trail length of control input which is different from system state and output at a specific iteration. In addition, the identical initial condition widely used in conventional ILC design is also mitigated. An example manifests the validity of the proposed ILC algorithm.

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