State-feedback MPC with piecewise constant control for continuous-time nonlinear systems

A new Model Predictive Control (MPC) algorithm for nonlinear systems is presented. The plant under control, the state and control constraints and the performance index to be minimized are described in continuous time, while the manipulated variables are allowed to change at fixed and uniformly distributed sampling times. In so doing, the optimization is performed with respect to sequences, as in discrete time nonlinear MPC, but the continuous time evolution of the system is considered and the approximate plant discretization is avoided, as in continuous time nonlinear AIPC.