Finite horizon nonlinear predictive control by Taylor approximation

In control system, the practical interest is driven by the fact that today’s processes need to be operated under tighter performance specifications. Often these demands can only be met when process nonlinearities are explicitly considered in the controller. Nonlinear predictive control, the extension of well established linear predictive control to the nonlinear systems, appears to be a well suited approach for this kind of problems. In this paper the optimal nonlinear predictive control structure is presented, which provides asymptotic tracking of smooth reference trajectories. The controller is based on a finite horizon continuous time minimization of nonlinear predicted tracking errors. A key feature of the control law is that its implementation does not need to perform an on line optimization, and asymptotic tracking of smooth reference signal is guaranteed. The proposed control scheme is applied to the trajectory tracking problem of a rigid link manipulator. Simulations results are performed to validate the tracking performance and robustness of the proposed controller