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RBFNN-based adaptive control of singular systems via non-fragile proportional and derivative feedback method

Author

Listed:
  • Zhang, Huiyan
  • Huang, Yu
  • Zhao, Ning
  • Mathiyalagan, Kalidass
  • Shi, Peng

Abstract

This paper investigates the issue of adaptive neural non-fragile proportional and derivative (PD) feedback control for the singular systems with unknown nonlinear dynamics. First, considering the inaccuracy of controller implementation, the problem of non-fragile controller design is considered and solved by using a robust control strategy. Second, PD feedback control is established to transform the singular system into a normal system, which facilitates stability analysis of the system. Third, the adaptive proportional–derivative radial basis function neural network technique is used to approximate the unknown nonlinear function and resist its influence. Under this designed framework, the stability conditions of the closed-loop system are given by using the Lyapunov method. The designed methods of state feedback gains and observer-based gain matrices are presented, respectively. Last, three examples are employed to elucidate the feasibility of the developed control strategy.

Suggested Citation

  • Zhang, Huiyan & Huang, Yu & Zhao, Ning & Mathiyalagan, Kalidass & Shi, Peng, 2026. "RBFNN-based adaptive control of singular systems via non-fragile proportional and derivative feedback method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 243(C), pages 51-68.
    • Handle: RePEc:eee:matcom:v:243:y:2026:i:c:p:51-68
    DOI: 10.1016/j.matcom.2025.11.021
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    References listed on IDEAS

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    1. Tian, Yongjie & Zhang, Huiyan & Liu, Yongchao & Zhao, Ning & Mathiyalagan, Kalidass, 2024. "Dynamic event-triggered adaptive neural control for MIMO nonlinear CPSs with time-varying parameters and deception attacks," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
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