Brief paper: An integrated perturbation analysis and Sequential Quadratic Programming approach for Model Predictive Control

Authors:
Reza Ghaemi;Jing Sun;Ilya V. Kolmanovsky
Affiliations:
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States;Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI 48109, United States;Ford Motor Company, 2101 Village Road, Dearborn, MI 48124, United States
Venue:
Automatica (Journal of IFAC)
Year:
2009

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Abstract

Computationally efficient algorithms are critical in making Model Predictive Control (MPC) applicable to broader classes of systems with fast dynamics and limited computational resources. In this paper, we propose an integrated formulation of Perturbation Analysis and Sequential Quadratic Programming (InPA-SQP) to address the constrained optimal control problems. The proposed algorithm combines the complementary features of perturbation analysis and SQP in a single unified framework, thereby leading to improved computational efficiency and convergence property. A numerical example is reported to illustrate the proposed method and its computational effectiveness.