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A scheduling quasi-min-max model predictive control algorithm for nonlinear systems
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DOI：10.1016/S0959-1524(01)00055-5.png)
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Abstract
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In this paper, a model predictive control algorithm is designed for nonlinear systems. Combination of a linear model with a linear parameter varying model approximates the nonlinear behavior. The linear model is used to express the current nonlinear dynamics, and the linear parameter varying model is used to cover the future nonlinear behavior. In the algorithm, a quasi-worst-case value of an infinite horizon objective function is minimized. Closed-loop stability is guaranteed when the algorithm is implemented in a receding horizon fashion by including a Lyapunov constraint in the formulation. The proposed approach is applied to control a jacketed styrene polymerization reactor. (C) 2002 Elsevier Science Ltd. All rights reserved.
Keywords:
scheduling
quasi-min-max
model predictive control (MPC)
nonlinear systems
linear matrix inequalities (LMIs)
linear parameter varying (LPV)
polytope updating
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