Data-based optimal coordination control of continuous-time nonlinear multi-agent systems via adaptive dynamic programming method

This paper focuses on the optimal coordination control problem for continuous-time nonlinear multi-agent systems with completely unknown dynamics via a data-based distributed adaptive dynamic programming method. As for most real-world applications, accurate system models are complicated to obtain, which restricts the application of the conventional methods. Moreover, it is challenging to design optimal coordination control of multi-agent systems especially for the time-varying communication topology. To deal with the difficulties, we investigate a distributed adaptive dynamic programming method with identifier-critic architecture under the switching communication topology. First, using the available system data, an online adaptive identifier is developed to approximate the unknown model dynamics, and simultaneously a critic neural network is employed for approximation of the optimal cost function, which yields approximated optimal coordination control in real time. Then, we analyze the stability of our proposed scheme. Eventually, the simulation illustrates the effectiveness of the developed method. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.