Iterative learning control for multi-agent systems with impulsive consensus tracking
Xiaokai Cao, Mičhal Fĕckan, Dong Shen, JinRong Wang
Abstract
In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed learning laws if the virtual leader has a path to any follower agent. Finally, an example is illustrated to verify the effectiveness by tracking a continuous or piecewise continuous desired trajectory.
Topics & Concepts
Iterative learning controlImpulse (physics)PiecewiseControl theory (sociology)Multi-agent systemTrajectoryComputer scienceTracking (education)Path (computing)State (computer science)Control (management)Mathematical optimizationMathematicsArtificial intelligenceAlgorithmPsychologyPhysicsMathematical analysisQuantum mechanicsProgramming languageAstronomyPedagogyIterative Learning Control SystemsNeural Networks Stability and SynchronizationNonlinear Dynamics and Pattern Formation