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Volume 8 Issue 6
Jun.  2021

IEEE/CAA Journal of Automatica Sinica

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J. X. Zhang, K. W. Li, and Y. M. Li, "Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1119-1132, Jun. 2021. doi: 10.1109/JAS.2021.1004018
Citation: J. X. Zhang, K. W. Li, and Y. M. Li, "Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1119-1132, Jun. 2021. doi: 10.1109/JAS.2021.1004018

Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints

doi: 10.1109/JAS.2021.1004018
Funds:  This work was supported by National Natural Science Foundation of China (61822307, 61773188)
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  • In this paper, an adaptive neural-network (NN) output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics, input saturation and state constraints. Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states. Under the framework of the backstepping design, by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function (BLF), the virtual and actual optimal controllers are developed. In order to accomplish optimal control effectively, a simplified reinforcement learning (RL) algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function, instead of employing existing optimal control methods. In addition, to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error, all state variables are confined within their compact sets all times. Finally, a simulation example is given to illustrate the effectiveness of the proposed control strategy.

     

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    Highlights

    • An adaptive NN optimal control scheme is developed for nonlinear systems with input saturation.
    • The Barrier type of optimal cost functions are constructed to solve the optimal control problem for nonlinear system.
    • By using the actor-critic architecture with tan-type BLF, the optimal controllers are designed by the backstepping recursive design.
    • Simplified RL algorithm is designed by deriving the updating laws from the simple positive function.

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