A Switched Systems Approach to Consensus of a Distributed Multi-agent System with Intermittent Communication

A novel switched systems approach is leveraged to enable a distributed multi-agent system to reach consensus under intermittent communication. A mobile information service provider (leader), that has full state feedback, switches between various (follower) agents lacking absolute position sensors to provide each follower with intermittent state information. The leader uses a neural network learning approach to develop a predictor of the location of the uncertain followers. Lyapunov-based analysis methods are used to show the followers asymptotically converge to a desired goal location. A novel switched systems analysis determines the maximum dwell-time the leader can allow each follower to drift from a predicted trajectory before state correction is necessary, despite the fact that the neural network predictor only achieves asymptotic convergence. Simulation results are included to validate the results.