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Nonlinear Model Predictive Control via Feasibility-Perturbed Sequential Quadratic Programming

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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.

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Authors and Affiliations

  1. Chemical Engineering Department, University of Wisconsin, Madison, WI, 53706, USA

    Matthew J. Tenny

  2. Computer Sciences Department, University of Wisconsin, 1210 W. Dayton Street, Madison, WI, 53706, USA

    Stephen J. Wright

  3. Chemical Engineering Department, University of Wisconsin, Madison, WI, 53706, USA

    James B. Rawlings

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  1. Matthew J. Tenny
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  2. Stephen J. Wright
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  3. James B. Rawlings
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Tenny, M.J., Wright, S.J. & Rawlings, J.B. Nonlinear Model Predictive Control via Feasibility-Perturbed Sequential Quadratic Programming. Computational Optimization and Applications 28, 87–121 (2004). https://doi.org/10.1023/B:COAP.0000018880.63497.eb

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