Adaptive tracking control of high-orderMIMO nonlinear systems with prescribed performance

Abstract

In this paper, an observer-based adaptive prescribed performance tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without input saturation. A novel finite-time neural network disturbance observer is constructed to estimate the system uncertainties and external disturbances. To guarantee the prescribed performance, an error transformation is applied to transfer the time-varying constraints into a constant constraint. Then, by employing a barrier Lyapunov function and the backstepping technique, an observer-based tracking control strategy is presented. It is proven that using the proposed algorithm, all the closed-loop signals are bounded, and the tracking errors satisfy the predefined time-varying performance requirements. Finally, simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

Keywords

Adaptive tracking control / Prescribed performance / Input saturation / Disturbance observer / Neural network

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2021 Zhejiang University Press