Iterative learning consensus control of nonlinear impulsive distributed parameter multi-agent systems

Wu, Jing; Dai, Xisheng; Tian, Senping; Huang, Qingnan

doi:10.1016/j.ejcon.2023.100785

Cited by 4 publications

(8 citation statements)

References 34 publications

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“…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.…”

Section: Problem Statementmentioning

confidence: 99%

“…Furthermore, appropriate ILC protocols are designed to diagnose faults within the MAS. To prove the convergence of this learning protocol, mathematical tools such as λ-norms, widely utilized in the convergence proof process, are employed, as seen in reference [36][37][38][39].…”

Section: Problem Statementmentioning

confidence: 99%

“…After selecting an appropriate learning gain, with the initial parameter set to r = 0, analysis and calculations reveal that these parameters satisfy the conditions of Theorems 1 and 2. Meanwhile, assuming the occurrence of fault in MAS (37), it is specified as follows:…”

Section: Numerical Simulationmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Zhou,

Wang

2024

Mathematics

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Component failures can lead to performance degradation or even failure in multi-agent systems, thus necessitating the development of fault diagnosis methods. Addressing the distributed fault diagnosis problem in a class of partial differential multi-agent systems with actuators, a fault estimator is designed under the introduction of virtual faults to the agents. A P-type iterative learning control protocol is formulated based on the residual signals, aiming to adjust the introduced virtual faults. Through rigorous mathematical analysis utilizing contraction mapping and the Bellman–Gronwall lemma, sufficient conditions for the convergence of this protocol are derived. The results indicate that the learning protocol ensures the tracking of virtual faults to actual faults, thereby facilitating fault diagnosis for the systems. Finally, the effectiveness of the learning protocol is validated through numerical simulation.

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