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Enhanced Sliding Variable-Based Robust Adaptive Control for Canonical Nonlinear System with Unknown Dynamic and Control Gain

Author

Listed:
  • Jiahao Zhu

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Kalyana C. Veluvolu

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

Abstract

This study presents an advanced Sliding Variable-Based Robust Adaptive Control (SVRAC) scheme designed for canonical nonlinear system with unknown dynamic and control gain functions. Leveraging neural network (NN) approximation, the proposed method simplifies control design by eliminating the need for traditional sliding mode control (SMC) components like equivalent and switching controls. SVRAC integrates three key elements: a feedback control term to stabilize system errors, a NN-based term to estimate and compensate for uncertainties, and a robustness adjustment term to maintain control integrity under dynamic variations. Theoretical validation through Lyapunov stability analysis confirms that the system errors are Semi-Globally Uniformly Ultimately Bounded (SGUUB), and the tracking error converges to a neighborhood of zero. Numerical and engineering simulations further demonstrate that SVRAC achieves superior tracking performance, robustness, and adaptability compared to conventional methods. This approach offers a streamlined yet effective solution for managing uncertainties in complex nonlinear systems, with potential applications across diverse engineering domains.

Suggested Citation

  • Jiahao Zhu & Kalyana C. Veluvolu, 2025. "Enhanced Sliding Variable-Based Robust Adaptive Control for Canonical Nonlinear System with Unknown Dynamic and Control Gain," Mathematics, MDPI, vol. 13(6), pages 1-15, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:976-:d:1613311
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    References listed on IDEAS

    as
    1. Jun Hu & Hongxu Zhang & Hongjian Liu & Xiaoyang Yu, 2021. "A survey on sliding mode control for networked control systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(6), pages 1129-1147, April.
    2. Liu, Chang & Wei, Tengda & He, Xinyi & Li, Xiaodi, 2024. "Sliding-mode control for target tracking of omnidirectional mobile robots subject to impulsive deception attacks," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    3. Qinqi Xu & Haijuan Zhao, 2024. "Sliding Mode Control of Uncertain Switched Systems via Length-Limited Coding Dynamic Quantization," Mathematics, MDPI, vol. 12(23), pages 1-18, November.
    4. Muhamad Deni Johansyah & Aceng Sambas & Fareh Hannachi & Seyed Mohamad Hamidzadeh & Volodymyr Rusyn & Monika Hidayanti & Bob Foster & Endang Rusyaman, 2024. "Dynamics and Stabilization of Chaotic Monetary System Using Radial Basis Function Neural Network Control," Mathematics, MDPI, vol. 12(24), pages 1-20, December.
    5. Yunmei Fang & Fang Chen & Juntao Fei, 2021. "Multiple Loop Fuzzy Neural Network Fractional Order Sliding Mode Control of Micro Gyroscope," Mathematics, MDPI, vol. 9(17), pages 1-20, September.
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