Optimal Iterative Learning Control Based on a Time-Parametrized Linear Time-Varying Model for Batch Processes

2013 ◽  
Vol 52 (18) ◽  
pp. 6182-6192 ◽  
Author(s):  
Zuhua Xu ◽  
Jun Zhao ◽  
Yi Yang ◽  
Zhijiang Shao ◽  
Furong Gao
Keyword(s):  
Iterative Learning Control ◽  
Linear Time ◽  
Learning Control ◽  
Iterative Learning ◽  
Batch Processes ◽  
Time Varying

A New Iterative Learning Control Scheme for Linear Time-varying Discrete Systems

2007 ◽  
Vol 40 (14) ◽  
pp. 279-282 ◽  
Author(s):  
Lukasz Hladowski ◽  
Krzysztof Galkowski ◽  
Eric Rogers ◽  
Paul L. Lewin ◽  
Christopher T. Freeman
Keyword(s):  
Iterative Learning Control ◽  
Linear Time ◽  
Discrete Systems ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Varying ◽  
Control Scheme

Iterative Learning Identification/Iterative Learning Control for Linear Time-Varying Systems

2016 ◽  
Vol 138 (10) ◽  
Author(s):  
Nanjun Liu ◽  
Andrew Alleyne
Keyword(s):  
Iterative Learning Control ◽  
Linear Time ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Parameter Estimates ◽  
Time Varying ◽  
Fixed Parameter ◽  
Time Varying Systems ◽  
And Control

This paper integrates a previously developed iterative learning identification (ILI) (Liu, N., and Alleyne, A. G., 2016, “Iterative Learning Identification for Linear Time-Varying Systems,” IEEE Trans. Control Syst. Technol., 24(1), pp. 310–317) and iterative learning control (ILC) algorithms (Bristow, D. A., Tharayil, M., and Alleyne, A. G., 2006, “A Survey of Iterative Learning Control,” IEEE Control Syst. Mag., 26(3), pp. 96–114), into a single norm-optimal framework. Similar to the classical separation principle in linear systems, this work provides conditions under which the identification and control can be combined and guaranteed to converge. The algorithm is applicable to a class of linear time-varying (LTV) systems with parameters that vary rapidly and analysis provides a sufficient condition for algorithm convergence. The benefit of the integrated ILI/ILC algorithm is a faster tracking error convergence in the iteration domain when compared with an ILC using fixed parameter estimates. A simple example is introduced to illustrate the primary benefits. Simulations and experiments are consistent and demonstrate the convergence speed benefit.

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