AAS 98-121

STRUCTURED MODEL REFERENCE ADAPTIVE CONTROL IN THE PRESENCE OF BOUNDED DISTURBANCES

M. R. Akella, J. L. Junkins - Texas A&M University

Abstract

We present a novel method to formulate and implement model reference adaptive control of poorly determined systems (for example, imprecise knowledge of the plant matrices) that are subject to bounded unknown disturbances. The disturbance dynamics are imposed on the system by augmenting its states to those of a nonlinear Markov process. Linearly contained coefficients in the generally nonlinear Markov process may be poorly known. Robust adaptive feedback control laws are then derived which ensure stability (bounded tracking errors) even in the presence of unknown model parameters, so long as the external disturbances belong to the class modeled by the nonlinear Markov process. We will demonstrate the practical implications of these results through numerical simulation of example problems.

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