Heterogeneous Multi-Agent-Based Fault Diagnosis Scheme for Actuation System

Abstract

In this paper, a fault diagnosis method of a heterogeneous multi-agent is proposed that realizes the rapid and accurate fault diagnosis of a redundant multi-type actuation system of large aircraft. Firstly, the multi-agent model of a large aircraft actuation system is established, the composition of the actuation system and the relationship between each multi-agent are clarified and three different types of actuator mathematical models are established. Secondly, a fault detection and isolation (FDI) model is established and transformed into an optimization problem according to different performance index requirements. Aiming at the optimization problem, combined with the principle of linear matrix inequality (LMI), the fault diagnosis algorithm of a heterogeneous multi-agent system is designed. Moreover, the threshold judgment method based on the error signal is presented. Finally, the three actuator models of the aileron actuation system of large aircraft are combined to complete the fault diagnosis of a heterogeneous multi-agent system under the given model interference and model fault. The obtained results demonstrate and validate that the proposed method can accurately and effectively diagnose the faults of the actuator and its associated actuators.

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

“…Utilizing the finite difference method for the differential equations and applying the previously designed fault estimator (11) and ILC protocol (12), the corresponding simulation results are illustrated in Figures 2-8.…”

“…The successful completion of complex tasks through cooperation in MASs is contingent upon the normal operation of each agent and the maintenance of regular communication connections between agents [7]. However, unlike the single intelligent system described by ordinary differential equations (ODEs) in [8][9][10], with an increase in the number of agents, the long-term operation of MASs becomes more susceptible to the influence of failures in practice [11]. Suppose that faults should be promptly diagnosed and addressed after occurrence, the distributed nature of MASs makes faults prone to propagating among their networks, leading to MAS paralysis and causing severe economic losses [12].…”

A New Topology-Switching Strategy for Fault Diagnosis of Multi-Agent Systems Based on Belief Rule Base

“…In this case, observer-based fault-diagnosis methods may not be applicable. Authors in [ 15 ] presented a data-driven state prediction and fault classification method based on the BP neural network model by using the residual signal designed the threshold, and then the fault diagnosis is carried out. The knowledge-based methods analyze the system, do not require an accurate model of the system, instead using information about the system and expert knowledge in the relevant field to achieve fault diagnosis, and is applicable to many complex systems [ 16 , 17 ].…”