Robust static output feedback infinite horizon RMPC for linear uncertain systems

Abstract

This work studies the problem of static output feedback (SOF) infinite horizon robust model predictive control (RMPC) for linear uncertain systems with input constraints. In contrast with the existing RMPC design methods, this approach computes the vector of control actions directly from the observed variables while the system states are unmeasurable. For the unavailable states, an ellipsoidal set is used to obtain an estimation and applied in the RMPC optimization problem. This set is updated at every time instant which may cause the loss of the recursive feasibility. In order to handle this drawback, a new design condition is adopted. Then, the design method is formulated in terms of a convex optimization problem with linear matrix inequality (LMI) constraints. The effectiveness of the proposed RMPC method is demonstrated by a numerical example.