Weighting matrix design for robust monotonic convergence in Norm Optimal iterative learning control

2008 ◽  
Author(s):  
Douglas A. Bristow
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Weighting Matrix ◽  
Monotonic Convergence ◽  
Convergence In Norm ◽  
Matrix Design

scholarly journals Simulation-based design of monotonically convergent iterative learning control for nonlinear systems

2012 ◽  
Vol 22 (4) ◽  
pp. 467-480
Author(s):  
Kamen Delchev
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Feedback Controllers ◽  
Model Based ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Time Varying Systems ◽  
Monotonic Convergence

This paper deals with a simulation-based design of model-based iterative learning control (ILC) for multi-input, multi-output nonlinear time-varying systems. The main problem of the implementation of the nonlinear ILC in practice is possible inadmissible transient growth of the tracking error due to a non-monotonic convergence of the learning process. A model-based nonlinear closed-loop iterative learning control for robot manipulators is synthesized and its tuning depends on only four positive gains of both controllers - the feedback one and the learning one. A simulation-based approach for tuning the learning and feedback controllers is proposed to achieve fast and monotonic convergence of the presented ILC. In the case of excessive growth of transient errors this approach is the only way for learning gains tuning by using classical engineering techniques for practical online tuning of feedback gains

scholarly journals A Design of Parameter Optimal Iterative Learning Control for Linear Discrete-Time Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Wataru Kase
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Gain ◽  
Quadratic Cost ◽  
Discrete Time Systems ◽  
Monotonic Convergence ◽  
Time Systems ◽  
Quadratic Cost Function

The iterative learning control algorithm proposed by Owens and Feng, which guarantees the monotonic convergence of the tracking error norms along with the trial, will be modified. The learning gain of the proposed method will be determined through a quadratic cost function. MIMO plant case will be also discussed. Numerical simulations will be presented to confirm the validity of the proposed design.

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