Robust stability constrained model predictive control with state estimation

A robust model predictive control (MPC) scheme based on output feedback is presented in this paper. A state estimator is incorporated in the MPC formulation to reflect the fact that the process output instead of state information is available. This approach is shown to asymptotically stabilize an uncertain linear plant with polytopic model uncertainty description. Linear matrix inequality (LMI) robust stability criteria are explicitly imposed in on-line computation as contractive constraints on the estimated state variables. The feasibility of these constraints can be detected either off-line or at the first step of on-line optimization. Comparing to other existing robust MPC formulations, the feasibility is independent of the selection of the optimization objective function and its parameters. Therefore, the objective function can be formulated based on other criteria such as performance requirements. The simulation study shows the effectiveness of this proposed method