Abstract
This paper proposes a novel finite-time optimal control method based on input–output data for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. In this method, the single-hidden layer feed-forward network (SLFN) with extreme learning machine (ELM) is used to construct the data-based identifier of the unknown system dynamics. Based on the data-based identifier, the finite-time optimal control method is established by ADP algorithm. Two other SLFNs with ELM are used in ADP method to facilitate the implementation of the iterative algorithm, which aim to approximate the performance index function and the optimal control law at each iteration, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.
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Acknowledgments
This work was supported in part by the Open Research Project from SKLMCCS (Grant no. 20120106), the Fundamental Research Funds for the Central Universities (Grant no. FRF-TP-13-018A), the China Postdoctoral Science Foundation (Grant no. 2013M530527), and the National Natural Science Foundation of China (Grants no. 61304079, 61125306, 61034002, 61374105), and Beijing Natural Science Foundation (Grant no. 4132078).
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Communicated by D. Liu.
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Song, R., Xiao, W., Wei, Q. et al. Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems. Soft Comput 18, 1645–1653 (2014). https://doi.org/10.1007/s00500-013-1170-z
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DOI: https://doi.org/10.1007/s00500-013-1170-z