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Self-optimizing robust nonlinear model predictive control

By M. Lazar, W. P. M. H. Heemels and A. Jokic

Abstract

Abstract. This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant- a unique feature in MPC. Moreover, the proposed MPC algorithm is computationally efficient for nonlinear systems that are affine in the control input and it allows for a decentralized implementation

Topics: nonlinear systems, robust model predictive control (MPC, input-tostate stability (ISS
Publisher: Springer
Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.188.9925
Provided by: CiteSeerX
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