Iterative learning based consensus control for distributed parameter type multi-agent differential inclusion systems with time-delay

Iterative learning based convergence analysis for nonlinear impulsive differential inclusion systems with randomly varying trial lengths

“…Next, to more clearly compare the effects of learning laws (25), (26) and (27) for the trend of tracking error, fixing parameter 𝜉 and set different m to obtain Figure 6 and Table 3 by numerical simulation. Obviously, compared with the learning law (25), the learning laws ( 26) and ( 27) make the convergence efficiency of the tracking error higher. This shows that the old historical information will reduce the convergence efficiency of the tracking error to a certain extent.…”

“…Liu et al 23 applied the ILC method to differential inclusion systems and studied the finite‐time trajectory tracking problem by using the set‐valued mapping and mathematical analysis tools. Other results can be referred to References 24–27.…”

Necessary and sufficient conditions for mean‐square bounded consensus of multiagent systems with additive noise

“…where P is a solution of (16). If  has a spanning tree and (B 1 , B 2 , B 4 ) is stabilizable, then the system (12) with a protocol ( 13) is MSBD.…”

“…In order to control the research cost and solve the problem of large number and complex structure of agents, an unbiased gradient technique is proposed in [15]. In addition, time delay and switching topologies will also affect the consensus of MASs [16][17][18].…”