Improved dynamic output feedback RMPC for linear uncertain systems with input constraints

Summary In this work, we propose a dynamic output feedback robust model predictive control (RMPC) design method for linear uncertain systems with input constraints. In order to handle the input constraints, the control signals are permitted to saturate, which can fully utilize the capability of actuators and thus can reduce the conservatism. For the unavailable states, an ellipsoidal set is used to obtain an estimation, and it is updated at every time instant. A modified RMPC design requirement is used to ensure the recursive feasibility of the optimization problem. Then, the design method is formulated in terms of a convex optimization problem with linear matrix inequality constraints. The proposed output feedback RMPC design method is expected to further reduce the conservativeness. The improvements of the proposed algorithm over the other existing techniques is demonstrated by an example.