IEEE Trans Cybern. 2023 May;53(5):3376-3387. doi: 10.1109/TCYB.2022.3226873. Epub 2023 Apr 21.
This article is concerned with the dynamic event-triggered-based adaptive output-feedback tracking control problem of nonlinear multiagent systems with time-varying input delay. By utilizing the approximation capability of neural network (NN), a low-gain nonlinear observer is first established to estimate the immeasurable states. To mitigate the effect of time-varying input delay, an auxiliary system with communication information is designed to generate the compensation signals. Then, a distributed adaptive composite NN dynamic surface control (DSC) strategy is proposed to acquire the satisfactory tracking accuracy, where the filter errors are compensated by the introduced serial-parallel estimation model. Moreover, an effective switching dynamic event-triggered mechanism is developed to determine the communication instants and reduce the update frequency of the controller. It is proven that the consensus tracking error converges to a residual set of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed composite NN DSC scheme.
这篇文章关注的是具有时变输入延迟的非线性多智能体系统的基于动态事件触发的自适应输出反馈跟踪控制问题。通过利用神经网络（NN）的逼近能力，首先建立一个低增益非线性观测器来估计不可测量的状态。为了减轻时变输入延迟的影响，设计了一个带有通信信息的辅助系统来生成补偿信号。然后，提出了一种分布式自适应复合神经网络动态面控制（DSC）策略，以获得满意的跟踪精度，其中引入的串联-并行估计模型补偿了滤波器误差。此外，开发了一种有效的切换动态事件触发机制来确定通信时刻并降低控制器的更新频率。证明了共识跟踪误差收敛到原点的残差集。最后，给出了仿真结果，以验证所提出的复合神经网络 DSC 方案的有效性。
