A Switched Systems Approach to Multiagent System Consensus: A Relay–Explorer Perspective

Abstract

The rendezvous or position consensus problem is a fundamental topic within the multiagent system (MAS) literature and has numerous engineering applications. The majority of recent results that solve the position consensus problem rely on communication networks and each agent's ability to obtain position information via direct measurements and communication. Distributed coordination strategies for MASs that are network free and have agents that cannot directly measure position information are scarce. In this work, we develop a relay–explorer strategy to achieve position consensus at a common, desired location. In particular, a group of explorer agents, lacking global position sensors, use open-loop estimators of their position to independently dead reckon toward the desired location. To prevent the difference between the estimated and true position of each explorer from growing beyond a user-defined threshold, a mobile information service provider (relay agent) that is capable of measuring its position in the global coordinate frame, intermittently visits each explorer to provide position information as determined by a maximum dwell-time condition. The relay agent has a position estimator for each explorer, and each estimator is synchronized with the corresponding explorer. The relay agent uses these position estimators to locate each explorer, maneuver to them, and provide position feedback. The contribution of this work over our precursory results is the consideration of uncertain explorer agent dynamics, which are estimated online using recurrent neural networks and integral concurrent learning. The estimated dynamics serve as feed-forward model approximations in the position estimators used by the explorers and relay agent, which generate more accurate position estimates once a finite excitation condition is satisfied. The MAS is modeled as a switched system, and a Lyapunov-based analysis is used to derive a maximum dwell-time condition for each explorer, prove the MAS is exponentially regulated to the desired location, and show the error between the estimated and true explorer dynamics is uniformly ultimately bounded. Experiments and multiple simulation examples are provided to verify the theoretical development, explore the scalability and learning performance of the approach, and shed light on future extensions.