A simple framework for nonlinear robust output-feedback MPC

In this paper, we present a simple methodology to design nonlinear robust output-feedback model predictive control (MPC) schemes. The design procedure is applicable to a large class of nonlinear systems and guarantees constraint satisfaction despite noise and disturbances. We utilize an existing observer with guaranteed exponential stability properties in combination with an initial bound on the estimation error in order to predict valid bounds on the possible future estimation error. The predicted estimation error is then used online to appropriately tighten the state and input constraints, using recently developed nonlinear robust MPC methods based on incremental stabilizability properties. The resulting nonlinear robust output-feedback MPC scheme is simple to implement, only marginally increases the computational demand (compared to a nominal MPC scheme), and ensures robust constraint satisfaction and input-to-state stability w.r.t. disturbances/noise. We demonstrate the simplicity and applicability of the proposed approach with a numerical example.