A design method for nonlinear model predictive control with a step-type input constraint

The nonlinear model predictive control needs to solve a two-point boundary-value problem (TP-BVP) at every sample time based on the receding horizon control strategy. However, solving a nonlinear algebraic equation for the TP-BVP requires high computational load, so computing the control law in real-time is a significant issue on the nonlinear model predictive control. This paper, therefore, proposes a design method for nonlinear model predictive control with a step-type input constraint. The proposed method searches an optimal control law under a step-type input signal constraint. Hence, the number of unknown parameters in the nonlinear equation can be reduced to the number of inputs. The effectiveness of the proposed method is demonstrated through a numerical simulation.