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Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

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Xiong Yang and Bo Zhao, "Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 575-583, Mar. 2020. doi: 10.1109/JAS.2020.1003063
Citation: Xiong Yang and Bo Zhao, "Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 575-583, Mar. 2020. doi: 10.1109/JAS.2020.1003063

Optimal Neuro-Control Strategy for Nonlinear Systems With Asymmetric Input Constraints

doi: 10.1109/JAS.2020.1003063
Funds:  This work was supported by the National Natural Science Foundation of China (61973228, 61973330)
More Information
  • In this paper, we present an optimal neuro-control scheme for continuous-time (CT) nonlinear systems with asymmetric input constraints. Initially, we introduce a discounted cost function for the CT nonlinear systems in order to handle the asymmetric input constraints. Then, we develop a Hamilton-Jacobi-Bellman equation (HJBE), which arises in the discounted cost optimal control problem. To obtain the optimal neurocontroller, we utilize a critic neural network (CNN) to solve the HJBE under the framework of reinforcement learning. The CNN’s weight vector is tuned via the gradient descent approach. Based on the Lyapunov method, we prove that uniform ultimate boundedness of the CNN’s weight vector and the closed-loop system is guaranteed. Finally, we verify the effectiveness of the present optimal neuro-control strategy through performing simulations of two examples.

     

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    Highlights

    • An optimal neural control is proposed for nonlinear systems with asymmetric input constraints.
    • This paper introduces a discounted-cost function to tackle asymmetric input constraints.
    • Only a critic neural network is utilized to implement the present optimal neuro-control scheme.
    • Uniform ultimate boundedness stability of all the signals in closed-loop system is proved.

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