Iterative learning based consensus control for distributed parameter type multi‐agent differential inclusion systems

Abstract

This article considers the learning consensus problem of distributed parameter type multi‐agent differential inclusion systems including parabolic type and hyperbolic type. By imposing a Lipschitz condition on a set‐valued mapping and utilizing distributed P$$ P $$‐type iterative learning consensus control protocols, an iterative learning process is established. Further, sufficient conditions for the convergence of the consensus error between any two agents in the sense of the certain norm are obtained with the help of Steiner‐type selectors. Finally, numerical simulations verify the validity of the proposed method.

Distributed Fault Diagnosis via Iterative Learning for Partial Differential Multi-Agent Systems with Actuators

“…The mathematical symbols and lemmas in the Preliminaries lay the foundation for subsequent analysis and description. Based on references [13][14][15][16][17][36][37][38][39], the dynamic model is constructed in the problem statement, addressing the fault diagnosis problem for a class of partial differential MASs. Furthermore, appropriate ILC protocols are designed to diagnose faults within the MAS.…”

“…However, in the real world, the state of MASs is not only time-dependent but also spatially influenced, as observed in examples such as flexible robotic arms, the axial movement of motors, and spacecraft surfaces, as referenced in [32][33][34][35]. Consequently, in recent years, significant efforts have been dedicated to addressing the control issues of MASs modeled by partial differential equations (PDEs) [36][37][38][39]. In [36], considering the spatiotemporal dynamic evolution of agents, the partial differential MAS dynamic model was established through PDEs to describe this behavior.…”

“…In [37], the consensus problem of a class of nonlinear pulse partial MASs was addressed by applying ILC methods. Furthermore, [38] and [39] employed ILC schemes to solve the consensus control problem of discretized partial differential MASs, respectively. This indicates that ILC can effectively address the coordinated control issues of partial differential MASs.…”

Data‐driven bipartite leader‐following consensus control for nonlinear multi‐agent systems under hybrid attacks

“…Recently, the cooperative control problem of multi‐agent systems (MASs) has received extensive attention for its practical applications in various areas, such as spacecrafts, 1,2 unmanned aerial vehicles, 3–5 robot systems, 6–8 and so on. Consensus control, one of the hottest cooperative issues, is aimed at addressing how to keep the states or outputs of MASs consistent, which only depends on local information 9–11 . In Reference 12, the problem of event‐triggered consensus control was studied for MASs with lossy sensors and cyber‐attacks.…”

“…Consensus control, one of the hottest cooperative issues, is aimed at addressing how to keep the states or outputs of MASs consistent, which only depends on local information. [9][10][11] In Reference 12, the problem of event-triggered consensus control was studied for MASs with lossy sensors and cyber-attacks. The distributed consensus control method based on a local composite disturbance observer was proposed for a class of disturbed second-order MASs with directed networks in Reference 13.…”