首页|Distributed event-triggered fault estimation for Takagi-Sugeno multi-agent systems with unmeasurable decision variables
Distributed event-triggered fault estimation for Takagi-Sugeno multi-agent systems with unmeasurable decision variables
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
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 actuators and sensors. To address these challenges, the Takagi-Sugeno (T-S) system model employed, incorporating unmeasurable decision variables, which introduces more complexity compared to known decision variables. This study pioneers the one-sided Lipschitz approach 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 data-driven. Agents can update their observers by using relative residual outputs derived neighboring information, enhancing both fault and state estimation accuracy. Furthermore, 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.
NETL
NSTL
Elsevier
