Iterative learning control for multi-agent systems with impulsive consensus tracking

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.

Relative controllability of multiagent systems with pairwise different delays in states

“…Following from the solution, Gramian criterion of relative controllability is established, and rank criterion is also yielded for the single-delayed system without pairwise matrices permutation. This work guarantees that we can further explore the iterative learning control of the delayed multiagent systems (see more in [4]).…”

Consensus of nonlinear multiagent systems with uncertain disturbance under stochastic switching topologies

“…8,9 This is because there exist a large amount of group dynamical behaviors which can be modeled as a complex network. The researchers make effort to design a variety of control protocols to regulate the global dynamical behaviors of the agents for some objectives, for example, impulsive control, 10 containment control, 11 and optimization control. 12 Compared with the centralized control, the distributed control of multiagent systems only requires local information between neighboring individuals; there is no need to transmit information to the central server, which makes the multiagent systems more flexible, economical, and efficient.…”

“…This is because there exist a large amount of group dynamical behaviors which can be modeled as a complex network. The researchers make effort to design a variety of control protocols to regulate the global dynamical behaviors of the agents for some objectives, for example, impulsive control, 10 containment control, 11 and optimization control 12 …”

Iterative learning control for impulsive multi-agent systems with varying trial lengths

“…Recently, ILC laws have been extensively studied for various types of MASs [19]. Note that MASs with impulse can generate discontinuous inputs, thus it is still challenging to consider whether ILC can be successfully applied to collect the sampled error data from each agent and track continuous or discontinuous trajectory, i.e., achieving leaderfollowing consensus for nonlinear dynamics of MAS with impulse [4]. In addition, [7,8] used Lyapunov stability theory to analyze the coordination performance of MAS.…”