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<p>This paper addresses the output tracking control of uncertain discrete-time strict-feedback nonlinear systems under the round-robin protocol (RRP). The prevailing control framework is prone to singularity issues in strict-feedback systems under the RRP data-transmission mode. To mitigate this issue, a neural-network-based dynamic model is formulated to provide continuous state estimation. Subsequently, an adaptive neural controller is synthesized based on this dynamic model to mitigate misalignment issues caused by stale data. Additionally, a novel error variable is introduced to enable online tuning of the controller’s neural weights under RRP. Lyapunov-based stability analysis is conducted to demonstrate the robustness of the proposed approach. Simulation studies on a numerical model and a three-wheeled mobile vehicle validate the effectiveness of the proposed methodology. Comparative experiments on the numerical model reveal superior performance in tracking error, convergence speed, and control input.</p>

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Adaptive neural control of discrete-time nonlinear systems using the Round-Robin protocol for communication scheduling

  • 期刊论文

  • 发表日期：

    2026年1月20日

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  • Zilin Gao,
  • Xuelin Yin,
  • Longwang Huang,
  • Lizhi Liu,
  • Yongfu Li

摘要

This paper addresses the output tracking control of uncertain discrete-time strict-feedback nonlinear systems under the round-robin protocol (RRP). The prevailing control framework is prone to singularity issues in strict-feedback systems under the RRP data-transmission mode. To mitigate this issue, a neural-network-based dynamic model is formulated to provide continuous state estimation. Subsequently, an adaptive neural controller is synthesized based on this dynamic model to mitigate misalignment issues caused by stale data. Additionally, a novel error variable is introduced to enable online tuning of the controller’s neural weights under RRP. Lyapunov-based stability analysis is conducted to demonstrate the robustness of the proposed approach. Simulation studies on a numerical model and a three-wheeled mobile vehicle validate the effectiveness of the proposed methodology. Comparative experiments on the numerical model reveal superior performance in tracking error, convergence speed, and control input.