Robust Output Feedback MPC for Linear Systems via Interpolation Technique

Abstract This paper provides a simple approach to the problem of robust output feedback model predictive control (MPC) for linear discrete-time systems with state and input constraints, subject to bounded state disturbances and output measurement errors. The problem of estimating the state is addressed by using a fixed linear observer. The state estimation error converges and stays in some set of the error dynamics, which is taken into account in the design of MPC controllers. In the MPC optimization where the nominal system is considered, the constraints are tightened in a monotonic sequence such that the satisfaction of input and state constraints for the original system is guaranteed. Robust stability of an invariant set for the closed-loop original system is ensured. Furthermore, in order to reduce the inherent computational complexity of the MPC controller design, interpolation techniques are introduced in the proposed approach, where the resulting controller interpolates among several MPC controllers. This procedure leads to a relatively large domain of attraction even by employing short prediction horizons. Therefore, with short horizons, a low computational complexity is expected.