Zhao Huarong, Yu Hongnian, Peng Li
IEEE Trans Neural Netw Learn Syst. 2022 Jun 8;PP. doi: 10.1109/TNNLS.2022.3174885.
In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.
在本研究中，我们研究了一类未知非仿射非线性离散时间多智能体系统（MASs）的事件触发时变轨迹二分形成跟踪问题。我们首先通过采用伪偏导数技术获得了一个具有每个智能体动态参数的等效线性数据模型。然后，我们利用MASs的输入/输出数据，在不使用系统结构或任何动力学知识的情况下，提出了一种事件触发的分布式无模型自适应迭代学习二分形成控制方案。为了提高灵活性和网络通信资源利用率，我们构建了一种带有死区算子的基于观测器的事件触发机制。此外，我们严格证明了所提算法的收敛性，其中每个智能体的时变轨迹二分形成跟踪误差被减小到零附近的一个小范围内。最后，四项仿真研究进一步验证了所设计控制方法的有效性，表明所提方案也适用于齐次MASs以实现时变轨迹二分形成跟踪。
