Consensus seeking via iterative learning for multi-agent systems with switching topologies and communication time-delays

Abstract

SUMMARYThis paper deals with the high-precision consensus seeking problem of multi-agent systems when they are subject to switching topologies and varying communication time-delays. By combining the iterative learning control (ILC) approach, a distributed consensus seeking algorithm is presented based on only the relative information between every agent and its local (or nearest) neighbors. All agents can be enabled to achieve consensus exactly on a common output trajectory over a finite time interval. Furthermore, conditions are proposed to guarantee both exponential convergence and monotonic convergence for the resulting ILC processes of multi-agent consensus systems. In particular, the linear matrix inequality technique is employed to formulate the established convergence conditions, which can directly give formulas for the gain matrix design. An illustrative example is included to validate the effectiveness of the proposed ILC-motivated consensus seeking algorithm.

Formation Tracking via Iterative Learning Control for Multiagent Systems with Diverse Communication Time‐Delays

“…Remark . Assumption is common in ILC for multiagent systems such as [16,19]. If the condition is not met, we can think of it as a robust problem against initial shifts.…”

“…An ILC tracking strategy was proposed for nonlinear multiagent systems with communication time-delays in [15]. Using the 2D analysis, [16] obtained convergence conditions in terms of linear matrix inequality and addressed both exponential convergence and monotonic convergence problems associated with the ILC process corresponding to the proposed consensus learning protocol. In [17], a distributed ILC algorithm was proposed for multiagent systems subject to 2D switching topologies and varying communication time-delays.…”

“…However, the bounds of tracking errors were subject to the accuracy of the estimated delay in [15]. The works [16,18] discussed consensus problem of multiagent systems not formation tracking. And [19] showed that the convergence speed got slower as the increase of the time-delays rate.…”

Consensus control of output‐constrained multiagent systems with unknown control directions under a directed graph

“…Distributed control of multiagent systems has received significant attention from the control community due to its extensive potential applications such as unmanned air vehicles, multiple mechanical systems, and mobile robots . One of the primary interests is the distributed consensus of multiagent systems, in which all agents aim to reach a common final state by relying merely on local information exchanges . The initial strategies have concentrated on the consensus of single‐ and double‐integrator multiagents without the consideration of model uncertainties .…”

Consensus Tracking of Fractional‐Order Multiagent Systems via Fractional‐Order Iterative Learning Control

“…Among the control methods, iterative learning control (ILC) can achieve full tracking in a limited time for the repetitive system [26][27][28]. erefore, the ILC algorithm is applied for IOMAS to solve the consensus problem recently, especially for complex MASs [29], MASs with switching topologies and communication time delays [30], and nonlinear MASs [31].…”