Work

Iterative Learning Fault Diagnosis of Fractional-Order Nonlinear Multi-Agent Systems with Initial State Learning and Switching Topology

Year: 2026

Type: article

Abstract: This paper proposes an iterative learning framework for a class of fractional-order nonlinear multi-agent systems operating under directed iteration-varying switching topologies. To suppress trial-to-trial fluctuations in initial states, a P-type initial condition learning mechanism is integrated into the update law, enabling each agent to actively compensate for its own startup offset in each iteration. The study designs a distributed iterative learning protocol using only local neighbor information, and this protocol can simultaneously achieve fault estimation and diagnosis. By constructing a fractional-order system model and adopting the contraction-mapping analysis method, sufficient conditions are derived in this paper, which guarantee that both the fault error and initial condition error converge asymptotically to zero as the number of iterations approaches infinity. The proposed scheme, based on iterative learning fault estimation, can effectively handle unknown nonlinearities without relying on an accurate system model. Numerical simulation results further verify the effectiveness of the designed fault observer in achieving fault estimation.

Source: Fractal and Fractional

DOI: 10.3390/fractalfract10020106

Authors: Junjie Ma, Xiaoxiao Xu, Guangxu Wang, Shuai Cai, Xingyu Zhou, Shuyu Zhang

Institutions: Nantong University, Shandong University, Sun Yat-sen University

Language: English

Topic: Iterative Learning Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funders: Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Natural Science Research of Jiangsu Higher Education Institutions of China