Wang Xiaoan, Nie Xiaobing, Cao Jinde, Hua Liang
IEEE Trans Cybern. 2025 Sep 22;PP. doi: 10.1109/TCYB.2025.3607803.
This article investigates the prespecified performance consensus problem for a class of nonlinear multiagent systems (MASs) with unknown actuator faults. By employing a sensor-triggered mechanism and neural estimation algorithm, a novel leader-follower consensus protocol is devised for the nonlinear MASs. The developed sensor event-triggered mechanism comprises two parts, the first one is sensor event-triggered sampling, and the second one is event-triggered information transmission. Due to the presence of the sensor-triggered mechanism, the system states cannot be available in real time. In order to solve this challenge, a signal decomposition and compensation strategy is constructed to balance the intermittent sensor-sampled signals and the real system inputs. Furthermore, the considered actuator faults in each follower are not limited to be finite, the time, frequency and mode of the faults are also unknown. To address the unknown actuator faults in the nonlinear MASs, a resilient fault management mechanism is developed for each follower. Based on the managed actuator faults dynamics, some bounded estimation signals are constructed and the issue of "explosion of complexity" in the backstepping design procedure is eliminated through the application of nonlinear filters with compensation terms. Finally, simulation results are given to illustrate the effectiveness of developed control protocol.
本文研究了一类具有未知执行器故障的非线性多智能体系统（MASs）的预先指定性能一致性问题。通过采用传感器触发机制和神经估计算法，为非线性MASs设计了一种新颖的领导者-跟随者一致性协议。所开发的传感器事件触发机制包括两部分，第一部分是传感器事件触发采样，第二部分是事件触发信息传输。由于存在传感器触发机制，系统状态无法实时获取。为了解决这一挑战，构建了一种信号分解和补偿策略，以平衡间歇性传感器采样信号和实际系统输入。此外，每个跟随者中考虑的执行器故障不限于有限的，故障的时间、频率和模式也是未知的。为了解决非线性MASs中的未知执行器故障问题，为每个跟随者开发了一种弹性故障管理机制。基于管理后的执行器故障动态，构建了一些有界估计信号，并通过应用带有补偿项的非线性滤波器消除了反步设计过程中的“复杂性爆炸”问题。最后，给出了仿真结果以说明所开发控制协议的有效性。
