Robust adaptive model predictive control based on comparison model

Abstract

This paper proposes an adaptive model predictive control (MPC) algorithm for constrained linear systems, which updates the estimation of system parameters on-line and produces the control input subject to the given input/state constraints. This method is based on a robust MPC algorithm using comparison models, which enable us to estimate the prediction error bounds of uncertain systems, and an adaptive mechanism. First, a new parameter update method based on the moving horizon estimation is proposed, which allows us to predict the worst-case estimation error bound over the prediction horizon. Second, we propose an adaptive MPC algorithm developed by combining the on-line parameter estimation with MPC method based on the modified comparison model which is extended to be applicable to the time varying-case. This method guarantees the feasibility and the stability of the closed-loop systems in the presence of system constraints. Finally, a numerical example is given to demonstrate its effectiveness.