Distributed event-triggered fault estimation for Takagi–Sugeno multi-agent systems with unmeasurable decision variables

Cite as:
[1] Z. Wang and M. Chadli, “Distributed event-triggered fault estimation for Takagi–Sugeno multi-agent systems with unmeasurable decision variables,” Journal of the Franklin Institute, vol. 362, no. 8, p. 107689, May 2025, doi: 10.1016/j.jfranklin.2025.107689.

Abstract:
This paper proposes a novel distributed fault estimation framework for a class of nonlinear multi-agent systems (MASs), addressing time-varying multiplicative and additive faults in both actuators and sensors. To address these challenges, the Takagi–Sugeno (T–S) system model is employed, incorporating unmeasurable decision variables, which introduces more complexity compared to known decision variables. This study pioneers the one-sided Lipschitz approach in this context, offering significant advancements over the traditional Lipschitz method. A two-step design process is presented to estimate system states, faults, and external disturbances through an ℓth-order proportional-integral observer and a constrained least squares estimator, which is data-driven. Agents can update their observers by using relative residual outputs derived from neighboring information, enhancing both fault and state estimation accuracy. Furthermore, a dynamic event-triggered communication protocol enables efficient output sharing and reduces communication costs. The observer design conditions are formulated as an optimization problem constrained by linear matrix inequalities, ensuring robust H-infinity performance. Simulation results validate the effectiveness of the proposed method for robust fault estimation in nonlinear MASs.

Keywords:
Multi-Agent Systems, Fault estimation, Event-triggered, T–S systems, Unmeasurable decision variables

Block diagram of the distributed joint fault estimation

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