Consensus Tracking of Multi-agent Systems with Unknown Dynamics: An Observer-Based Adaptive Neural Networks Approach

This article reports the consensus tracking for multi-agent systems with unknown nonlinear dynamics and directed switching topology by using an observer-based adaptive neural network control approach. First, an observer together with a neural network adaptive law is designed to estimate the state of each agent. Second, a continuous feedback consensus control protocol is designed with the state of the observer and the adaptive neural network law. Finally, by using the average dwell time method and the adaptive neural network technology, the consensus tracking can be achieved in the proposed multi-agent system. Furthermore, this result is also extended to multi-agent systems with intermittent topology. Two numerical examples are given to demonstrate the validity of the theoretical results.