Neural-Network-Based Approach for Finite-Time Optimal Control

  • Ruizhuo SongEmail author
  • Qinglai Wei
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 166)


This chapter proposes a novel finite-time optimal control method based on input-output data for unknown nonlinear systems using 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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Copyright information

© Science Press, Beijing and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of Science and Technology BeijingBeijingChina
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina

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