Distributed optimal coordination control for nonlinear multi-agent systems using event-triggered adaptive dynamic programming method. (August 2019) - Zhao, Wei; Zhang, Huaipin
Abstract: 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. Highlights: Compared with time-triggered ADP methods (Vamvoudakis et al., 2012), the proposed ETADP method can alleviate the communication load and computation cost since the data transmissions and the critic NN weight are only updated at the trigger instants. The PI algorithm is implemented based on distributed asynchronous scheme. Compared with the critic-actor network framework (Abouheaf et al., 2014), the single critic network for each agent is proposed to approximate the value functions which simplifies the network structure and reduces the weight updated number.