Tractable robust model predictive control with adaptive sliding mode for uncertain nonlinear systems

Abstract

This paper proposes a tractable robust nonlinear model predictive control for continuous-time uncertain systems with stability guaranteed. The uncertainty is considered in parameters or additive form. First, a sampled-data model predictive control for the nominal system is designed to provide the desired performance. Then, an adaptive sliding mode control is designed to recover the nominal performance for the uncertain system. By merging sampled-data model predictive control and sliding mode control in-between samples, the effect of the uncertainty is reduced efficiently. The computational complexity of the proposed robust model predictive control is the same as for the model predictive control while asymptotic stability of the closed-loop system is achieved. The simulation results illustrate the effectiveness of the proposed approaches.