Robust model predictive control for nonlinear systems

A technique is introduced for the design of a robustly stabilizing model predictive output feedback controller for continuous time-varying nonlinear constrained systems. The controller is easily implementable and computationally inexpensive. A family of first order approximations of the plant is used for computation of the optimal control moves, which allows the optimization problem to be posed as a quadratic programming problem (QP). Through the use of a “stability constraint” the resulting control algorithm can be made asymptotically stable and is able to handle unknown time-varying disturbances, measurement bias and structural model/plant mismatch