MODEL REFERENCE PARAMETRIC ADAPTIVE ITERATIVE LEARNING CONTROL
H. Yu* M. Deng* T.C. Yang** D.H. Owens***
* School of Engineering and Computer Science, Exeter University, Exeter EX4 4QF Email: H.Yu@Exeter.ac.uk
** School of Engineering and Information Technology, Sussex University, Brighton BN1 9QT
*** Department of Automatic Control and Systems, Sheffield University, Sheffield S1 3JD
Most of iterative learning control (ILC) methods requires that the relative degree of the plant is less than 2 for a linear system or the plant is passive for a non-linear system. A new model reference parametric adaptive iterative learning control using the command generator tracker (CGT) theory is proposed in this paper. The method can be applied to control a plant with a higher relative degree and it only requires to iteratively adjust nm + 2 parameters (nm is the order of the reference model) for an SISO plant. Therefore, the ILC control system is very simple. The proposed method is in the spirit of simple adaptive control which has received intensive researches during past two decades. Simulation results show the effectiveness and usefulness of the proposed method.
Keywords: Iterative learning control, Adaptive control, Strictly positive real, Model reference control
Session slot T-Fr-A21: Posters of Learning, Stochastic, Fuzzy and Nerural Systems/Area code 3b : Adaptive Control and Tuning

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