Neural-network-based adaptive quasi-consensus of nonlinear multi-agent systems with communication constrains and switching topologies

Abstract The quasi-consensus problem is investigated for a class of nonlinear multi-agent systems with communication constrains and switching topologies, where each agent is assumed to share information only with its neighbors on some disconnected time intervals, and the underlying topology is time-varying. Due to the approximation capability of neural networks, the uncertain nonlinear dynamics is compensated by the adaptive neural network scheme. A novel neural-network-based adaptive intermittent control protocol is proposed based on each agent maintaining a neural network parametric approximator. Some novel and simple quasi-consensus criteria are derived by the Lyapunov stability theory and matrix analysis, it is proved that quasi-consensus can be reached if the measure of communication is larger than a threshold value. By the theoretical analysis, the consensus error can be reduced as small as desired. Then, the proposed method is used to consensus of multi-agent systems with known nonlinear dynamics. Finally, two simulation examples are provided to demonstrate the effectiveness of the obtained theoretical results.