A pole placement-based NMPC algorithm of constrained Hammerstein systems

This paper presents a new nonlinear model predictive control (NMPC) algorithm for Hammerstein systems subject to constraints on the state, input and intermediate variable. Taking no account of constraints, a desired linear controller of the intermediate variable is obtained by applying pole placement to the linear subsystem. Then, actual control actions are determined in consideration of constraints by on-line solving a finite horizon optimal control problem, where only the first control is calculated and others are approximated to reduce the computational demand. Moreover, the asymptotic stability can be guaranteed in some cases. Finally, an example of simulation is used to demonstrate the effectiveness of the proposed algorithm.