Abstract
The optimal control of uncertain fuzzy systems with constraints is still an open problem. One candidate to deal with this problem is robust receding horizon control (RRHC) schemes, which can be formulated as a differential game. Our focus concerns numerically solving Hamilton–Jacobi–Issac (HJI) equations derived from RRHC schemes for uncertain fuzzy systems. The developed finite difference approximation scheme with sigmoidal transformation is a stable and convergent algorithm for HJI equations. Accelerated procedures with boundary value iteration are developed to increase the calculation accuracy with less time consumption. Then, the state-feedback RRHC controller is designed for some class of uncertain fuzzy systems with constraints. The value function calculated by numerical methods acts as the design parameter. The closed-loop system is proven to be asymptotically stable. An engineering implementation of the controller is discussed.
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Acknowledgments
The authors would like to acknowledge Mr. Zhuo Wang Ph.D. and Xiuchong Liu for their useful discussion. This work was supported by the National Natural Science Foundation of China (Grant Nos. 60974141, 60504006, 60621001, 60728307, 60774093), the Natural Science Foundation of Liaoning Province (Grant No. 20092007) and the Fundamental Research Funds for the Central Universities (Grant Nos. N100404015, N100404012).
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Song, C., Ye, J. Robust receding horizon control of uncertain fuzzy systems. Neural Comput & Applic 22, 237–247 (2013). https://doi.org/10.1007/s00521-011-0710-7
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DOI: https://doi.org/10.1007/s00521-011-0710-7