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Single Parameter Adaptive Control of Unknown Nonlinear Systems with Tracking Error Constraints

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  • Hongjun Yang
  • Zhijie Liu
  • Shuang Zhang

Abstract

This paper investigates a single parameter adaptive neural network control method for unknown nonlinear systems with bounded external disturbances. A smooth performance function is developed to achieve the transient and steady state of system tracking error that could be constrained in prescribed bounds. The difficulties in dealing with unknown system parameters and disturbances of nonlinear systems are resolved based on the single parameter adaptive neural network control which is proposed to effectively reduce the calculation amount. The theoretical analysis implies that the proposed control scheme makes the closed-loop system uniformly ultimately bounded. Simulation demonstrates that the proposed adaptive controller gives a favorable performance on tracking desired signal and constraining the bounds of tracking error which could be arbitrarily small with appropriate adaptive parameters. Both the theoretical analysis and simulations confirm the effectiveness of the control scheme.

Suggested Citation

  • Hongjun Yang & Zhijie Liu & Shuang Zhang, 2018. "Single Parameter Adaptive Control of Unknown Nonlinear Systems with Tracking Error Constraints," Complexity, Hindawi, vol. 2018, pages 1-9, October.
    • Handle: RePEc:hin:complx:6457354
    DOI: 10.1155/2018/6457354
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    Cited by:

    1. Liu, Chong & Zhang, Huaguang & Luo, Yanhong & Zhang, Kun, 2021. "Echo state network-based online optimal control for discrete-time nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    2. Liu, Shanlin & Niu, Ben & Zong, Guangdeng & Zhao, Xudong & Xu, Ning, 2022. "Adaptive fixed-time hierarchical sliding mode control for switched under-actuated systems with dead-zone constraints via event-triggered strategy," Applied Mathematics and Computation, Elsevier, vol. 435(C).

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