Nonlinear Model Predictive Control via Feasibility-Perturbed Sequential Quadratic Programming

Authors:
Matthew J. Tenny;Stephen J. Wright;James B. Rawlings
Affiliations:
Chemical Engineering Department, University of Wisconsin, Madison, WI 53706, USA;Computer Sciences Department, 1210 W. Dayton Street, University of Wisconsin, Madison, WI 53706, USA;Chemical Engineering Department, University of Wisconsin, Madison, WI 53706, USA
Venue:
Computational Optimization and Applications
Year:
2004

Citing 5
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Abstract

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.