Zhao Wei, Zhang Huaipin
School of Mathematics, Southeast University, Nanjing, 210096, PR China.
Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, PR China.
ISA Trans. 2019 Aug;91:184-195. doi: 10.1016/j.isatra.2019.01.021. Epub 2019 Jan 24.
This paper is concerned with the design of distributed optimal coordination control for nonlinear multi-agent systems (NMASs) based on event-triggered adaptive dynamic programming (ETADP) method. The method is firstly introduced to design the distributed coordination controllers for NMASs, which not only avoids the transmission of redundant data compared with traditional time-triggered adaptive dynamic programming (TTADP) strategy and minimizes the performance function of each agent. The event-triggered conditions are proposed based on Lyapunov functional method, which is deduced by guaranteeing the stability of NMASs. Then a new adaptive policy iteration algorithm is presented to obtain the online solutions of the Hamiton-Jocabi-Bellman (HJB) equations. In order to implement the proposed ETADP method, the fuzzy hyperbolic model based critic neural networks (NN) are utilized to approximate the value functions and help calculate the control policies. In critic NNs, the NN weight estimations are updated at the event-triggered instants leading to aperiodic weight tuning laws so that computation cost is reduced. It is proved that the weight estimation errors and the local neighborhood coordination errors is uniformly ultimately bounded (UUB). Finally, two simulation examples are provided to show the effectiveness of the proposed ETADP method.
本文研究基于事件触发自适应动态规划（ETADP）方法的非线性多智能体系统（NMASs）分布式最优协调控制设计。该方法首先被用于设计NMASs的分布式协调控制器，与传统的时间触发自适应动态规划（TTADP）策略相比，它不仅避免了冗余数据的传输，还能使每个智能体的性能函数最小化。基于李雅普诺夫泛函方法提出了事件触发条件，通过保证NMASs的稳定性推导得出。然后提出了一种新的自适应策略迭代算法来获得哈密顿-雅可比-贝尔曼（HJB）方程的在线解。为了实现所提出的ETADP方法，利用基于模糊双曲模型的评判神经网络（NN）来逼近值函数并帮助计算控制策略。在评判神经网络中，神经网络权重估计在事件触发时刻进行更新，从而产生非周期的权重调整律，进而降低计算成本。证明了权重估计误差和局部邻域协调误差是一致最终有界（UUB）的。最后，给出了两个仿真例子以说明所提出的ETADP方法的有效性。
