Piece-wise constant predictive feedback control of nonlinear systems

We investigate the problem of receding horizon control for a class of nonlinear processes. A computationally efficient method is developed to identify the optimal control action with respect to predefined performance criteria. Using Carleman linearization and assuming piece-wise constant control action, the state vector is discretized explicitly in time. The optimal control problem is then reformulated as a nonlinear optimization problem and is efficiently solved using analytically computed sensitivity functions and standard gradient-based algorithms.