Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trust-region feasibility-perturbed sequential quadratic programming algorithm (developed in a companion report), then discuss its adaptation to the problems arising in nonlinear model predictive control. Computational experience with several representative sample problems is described, demonstrating the effectiveness of the proposed approach.
Optimization Technical Report 02-06, August, 2002, Computer Sciences Department, University of Wisconsin. Texas-Wisconsin Modeling and Control Consortium Report TWMCC-2002-02. Revised April, 2003. To appear in "Computational Optimization and Applications"
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