Iterative learning of model reference adaptive controller for uncertain nonlinear systems with only output measurement

Authors:
Chiang-Ju Chien;Chia-Yu Yao
Affiliations:
Department of Electronic Engineering, Huafan University, 223, Shihtin, Taipei County, Taiwan;Department of Electronic Engineering, Huafan University, 223, Shihtin, Taipei County, Taiwan
Venue:
Automatica (Journal of IFAC)
Year:
2004

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Abstract

In this paper, a model reference adaptive control strategy is used to design an iterative learning controller for a class of repeatable nonlinear systems with uncertain parameters, high relative degree, initial output resetting error, input disturbance and output noise. The class of nonlinear systems should satisfy some differential geometric conditions such that the plant can be transformed via a state transformation into an output feedback canonical form. A suitable error model is derived based on signals filtered from plant input and output. The learning controller compensates for the unknown parameters, uncertainties and nonlinearity via projection type adaptation laws which update control parameters along the iteration domain. It is shown that the internal signals remain bounded for all iterations. The output tracking error will converge to a profile which can be tuned by design parameters and the learning speed is improved if the learning gain is large.