Zhang Yibo, Wu Wentao, Chen Weixing, Lu Haibo, Zhang Weidong
IEEE Trans Cybern. 2024 Jul;54(7):4111-4123. doi: 10.1109/TCYB.2024.3351476. Epub 2024 Jul 11.
In this article, a distributed output-feedback consensus maneuvering problem is investigated for a class of uncertain multiagent systems with multi-input and multi-output (MIMO) strict-feedback dynamics. The followers are subject to immeasurable states and external disturbances. A distributed neural observer-based adaptive control method is designed for consensus maneuvering of uncertain MIMO multiagent systems. The method is based on a modular structure, resulting in the separation of three modules: 1) a variable update law for the parameterized path; 2) a high-order neural observer; and 3) an output-feedback consensus maneuvering control law. The proposed distributed neural observer-based adaptive control method ensures that all followers agree on a common motion guided by a desired parameterized path, and the proposed method evades adopting the adaptive backstepping or dynamic surface control design by reformulating the dynamics of agents, thereby reducing the complexity of the control structure. Combined with the cascade system analysis and interconnection system analysis, the input-to-state stability of the consensus maneuvering closed loop is established in the Lyapunov sense. A simulation example is presented to demonstrate the performance of the proposed distributed neural observer-based adaptive control method for output-feedback consensus maneuvering.
本文研究了一类具有多输入多输出（MIMO）严格反馈动力学的不确定多智能体系统的分布式输出反馈协同机动问题。跟随者受到不可测量的状态和外部干扰。针对不确定MIMO多智能体系统的协同机动，设计了一种基于分布式神经观测器的自适应控制方法。该方法基于模块化结构，将三个模块分离：1）参数化路径的变量更新律；2）高阶神经观测器；3）输出反馈协同机动控制律。所提出的基于分布式神经观测器的自适应控制方法确保所有跟随者在由期望参数化路径引导的共同运动上达成一致，并且所提出的方法通过重新构建智能体的动力学来避免采用自适应反步法或动态面控制设计，从而降低了控制结构的复杂性。结合级联系统分析和互联系统分析，在李雅普诺夫意义下建立了协同机动闭环的输入到状态稳定性。给出了一个仿真例子，以说明所提出的基于分布式神经观测器的自适应控制方法用于输出反馈协同机动的性能。
