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Contractive model predictive control for constrained nonlinear systems


S. De Oliveira, M. Morari

vol. AUT96-13

This paper addresses the development of stabilizing state and output feedback model predictive control (MPC) algorithms for constrained continuous-time nonlinear systems with discrete observations. Moreover, we propose a nonlinear observer structure for this class of systems and derive sufficient conditions under which this observer provides asymptotically convergent estimates. The MPC scheme proposed here consists of a basic finite horizon nonlinear MPC technique with the introduction of an additional state constraint which has been called contractive constraint. The resulting MPC scheme has been denoted contractive MPC (CNTMPC). This is a Lyapunov-based approach in which a Lyapunov function chosen a priori is decreased, not continuously, but discretely; it is allowed to increase at other times (between prediction horizons). We will show in this work that the implementation of this additional constraint into the on-line optimization makes it possible to prove strong nominal stability properties of the closed-loop system. In the absence of disturbances, it can be shown that the presence of the contractive constraint renders the closed-loop system exponentially stable in the state feedback case and uniformly asymptotically stable in the output feedback case. Keywords: model predictive control, nonlinear control, constrained control


Type of Publication:

(04)Technical Report

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% Autogenerated BibTeX entry
@TechReport { OliMor:1996:IFA_1498,
    author={S. De Oliveira and M. Morari},
    title={{Contractive model predictive control for constrained
	  nonlinear systems}},
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