Dynamic output feedback robust model predictive control with guaranteed quadratic boundedness under redefined bounds on the unknown true state for the system with polytopic uncertainty and bounded disturbance

This paper considers the dynamic output feedback robust model predictive control for a system with both polytopic uncertainty and bounded disturbance. Our previous studies on this topic have shown that the techniques for handling the unknown true state are crucial. It is required to utilize the bounds of the true state to replace the true state itself in the optimization problems. The previous approach, which defines an error signal as the linear combination of the true state, the estimated state and the output, is improved. By the improved procedure, an extended technique for handling the unknown true state is given for the main optimization problem, but greatly simplified technique is involved for the auxiliary optimization problem. Here, like our published works, the main optimization problem is utilized to refresh the control law parameters, but needs not be solved at every sampling instant, and the auxiliary optimization problem is solved to determine whether or not to refresh the solution of the approximate main optimization problem. By applying the proposed method, the augmented state of the closed-loop system is guaranteed to converge to the neighborhood of the equilibrium point.