Iterative learning control for discrete-time systems with exponential rate of convergence

1996 ◽  
Vol 143 (2) ◽  
pp. 217-224 ◽  
Author(s):  
N. Amann ◽  
D.H. Owens ◽  
E. Rogers
Keyword(s):  
Rate Of Convergence ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Exponential Rate ◽  
Discrete Time Systems ◽  
Exponential Rate Of Convergence ◽  
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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