Output feedback model predictive control for Hammerstein model with bounded disturbance

This paper proposes two model predictive control (MPC) methods for a Hammerstein with both unmeasurable state and bounded disturbance. First, dynamic output feedback MPC is designed such that the dynamics of estimation error decoupled from estimated state. Hence, separation principle holds part controller parameters can be off-line. Second, case considered where nonlinearity not exactly inverted, is, reserved static exists in closed-loop system. merged into polytopic description combination linear part. The move then parameterized as law followed by approximate inverse nonlinearity. recursive feasibility stability approaches are guaranteed. Numerical examples given order to show effectiveness proposed approaches.