Cooperative Learning for Switching Networks With Nonidentical Nonlinear Agents
Deyuan Meng, Jingyao Zhang
Abstract
This article is aimed at realizing cooperative learning for networked multiagent systems subject to uncertain nonlinear dynamics and switching topologies. A distributed control protocol is proposed by integrating the nearest neighbor rules and iterative updating rules. Thanks to cooperative learning, all agents can be ensured to track any prescribed reference robustly over any finite interval, regardless of the nonidentical locally Lipschitz nonlinearities of agents, initial state shifts, and external disturbances. Moreover, a convergence analysis approach to cooperative learning is given by exploring the properties for the products of stochastic matrices that are associated with switching digraphs.
Topics & Concepts
Lipschitz continuityComputer scienceIterative learning controlConvergence (economics)Multi-agent systemNetwork topologyNonlinear systemControl theory (sociology)Protocol (science)Topology (electrical circuits)Control (management)MathematicsArtificial intelligenceComputer networkQuantum mechanicsPathologyMathematical analysisEconomicsCombinatoricsMedicineEconomic growthAlternative medicinePhysicsDistributed Control Multi-Agent SystemsNeural Networks Stability and SynchronizationNonlinear Dynamics and Pattern Formation