A stabilizing model-based predictive control algorithm for nonlinear systems

Predictive control of nonlinear systems subject to state and input constrai nts is considered. Given an auxiliary linear control law, a good nonlinear receding-horizon controller should (i) be computationally feasible, (ii) en large the stability region of the auxiliary controller, and (iii) approxima te the optimal nonlinear infinite-horizon controller in a neighbourhood of the equilibrium. The proposed scheme achieves these objectives by using a p rediction horizon longer than the control one in the finite-horizon cost fu nction. This means that optimization is carried out only with respect to th e first few input moves whereas the state movement is predicted (and penali zed) over a longer horizon where the remaining input moves are computed usi ng the auxiliary linear control law. Closed-loop stability is ensured by me ans of a penalty on the terminal state which is a computable approximation of the infinite-horizon cost associated with the auxiliary controller. As a n illustrative example, the predictive control of a highly nonlinear chemic al reactor is discussed. (C) 2001 Elsevier Science Ltd. All rights reserved .