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Constrained predictive control of nonlinear stochastic systems

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2 Author(s)
Yin, Yanyan ; Institute of Automation, Jiangnan University, Wuxi 214122, P. R. China ; Liu, Fei

The receding horizon control (RHC) problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs. In the given receding horizon, for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter, the cost function is optimized by constraints on state trajectories, so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set. Out of the receding horizon, the stability is guaranteed by searching a state feedback control law. Based on such stability analysis, a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed. The simulation shows the validity of this method.

Published in:

Systems Engineering and Electronics, Journal of  (Volume:21 ,  Issue: 5 )

Date of Publication:

Oct. 2010

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