Output Feedback Model Predictive Tracking Control Using a Slope Bounded Nonlinear Model View Full Text


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Article Info

DATE

2014-01

AUTHORS

S. M. Lee, O. M. Kwon, Ju H. Park

ABSTRACT

In this paper, an output feedback model predictive tracking control method is proposed for constrained nonlinear systems, which are described by a slope bounded model. In order to solve the problem, we consider the finite horizon cost function for an off-set free tracking control of the system. For reference tracking, the steady state is calculated by solving by quadratic programming and a nonlinear estimator is designed to predict the state from output measurements. The optimized control input sequences are obtained by minimizing the upper bound of the cost function with a terminal weighting matrix. The cost monotonicity guarantees that tracking and estimation errors go to zero. The proposed control law can easily be obtained by solving a convex optimization problem satisfying several linear matrix inequalities. In order to show the effectiveness of the proposed method, a novel slope bounded nonlinear model-based predictive control method is applied to the set-point tracking problem of solid oxide fuel cell systems. Simulations are also given to demonstrate the tracking performance of the proposed method. More... »

PAGES

239-254

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10957-012-0201-8

DOI

http://dx.doi.org/10.1007/s10957-012-0201-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034143218


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