Robust output-feedback MPC with soft state constraints

In this paper, we present a robust output-feedback model predictive control (MPC) design for a class of open-loop stable systems with hard input- and soft state constraints. The proposed output-feedback design is based on a linear state estimator and a novel parameterization of the soft state constraints that has the advantage of leading to optimization problems of prescribable size. Robustness against unstructured model uncertainty is obtained by choosing the cost function parameters so as to satisfy a linear matrix inequality condition. The proposed output-feedback design incorporates a novel state-feedback design, which may be seen as a generalization of a previous proposal.