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<p>To balance the control performance and communication burden for uncertain strict-feedback systems with various input constraints, this paper develops an adaptive neural network (NN) dynamic event-triggered controller based on disturbance observers. Specifically, we introduce an auxiliary signal to compensate for the effect of input delay and input saturation simultaneously. Then, by constructing new disturbance observers, the composite disturbances consisted of approximation error caused by NN and external interference are estimated accurately. Furthermore, filtering errors neglected generally are reduced by the improved dynamic surface control technique. In addition, we add a dynamic event-triggered mechanism in the control scheme, which greatly improves the control efficiency. Theoretical analysis indicates that all closed-loop signals are bounded and the Zeno behavior is effectively avoided. Simulation examples are provided to verify the availability of the proposed method.</p>

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Disturbance Observer-Based Adaptive Neural Network Dynamic Event-Triggered Control for Nonlinear Systems with Input Constraints

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Qingling Wang,
  • Weiting Hua

摘要

To balance the control performance and communication burden for uncertain strict-feedback systems with various input constraints, this paper develops an adaptive neural network (NN) dynamic event-triggered controller based on disturbance observers. Specifically, we introduce an auxiliary signal to compensate for the effect of input delay and input saturation simultaneously. Then, by constructing new disturbance observers, the composite disturbances consisted of approximation error caused by NN and external interference are estimated accurately. Furthermore, filtering errors neglected generally are reduced by the improved dynamic surface control technique. In addition, we add a dynamic event-triggered mechanism in the control scheme, which greatly improves the control efficiency. Theoretical analysis indicates that all closed-loop signals are bounded and the Zeno behavior is effectively avoided. Simulation examples are provided to verify the availability of the proposed method.