多智能体系统的事件触发无模型迭代学习双向一致性

Event-triggered model-free adaptive iterative learning bipartite consensus control for multi-agent systems

针对未知模型的非线性离散时间多智能体系统,研究基于事件触发迭代学习双向一致性问题.首先,利用紧凑形式动态线性化方法,建立多智能体系统的动态线性化数据模型,提出一种该数据模型的参数估计算法;其次,基于该数据模型设计输出观测器和死区控制器,并结合信号图论,构建一种事件触发分布式无模型迭代学习双向一致性控制策略;然后,通过设计李雅普诺夫函数对该控制策略的收敛性进行严格的证明;最后,通过数值仿真进一步验证该控制协议的正确性和有效性.

This paper investigates the event-triggered distributed model-free iterative learning bipartite consensus(ETDMFILBC) problem for unknown nonlinear discrete-time multi-agent systems. A dynamics linearization data model is first established by employing a compact form dynamics linearization approach, and the estimation algorithm of its parameters is also formulated. After that, the output observer and dead-zone operator are designed. Meanwhile,applying the signed graph theory, an ETDMFILBC scheme is proposed. Moreover, the convergence property of the proposed algorithm is strictly proved using the constructed Lyapunov function. Finally, the results of the simulation further verify the correctness and effectiveness of the designed scheme.