Takagi-Sugeno fuzzy fault estimator design for nonlinear dynamics of autonomous ground vehicles

In the context of cloud and edge computing for real-time fault diagnosis (FD) in autonomous ground vehicles (AGVs), this paper proposes a novel fault estimation method for AGV actuators. A two degrees-of-freedom nonlinear vehicle model is first used to characterize the AGV dynamics. Based on this model, a Takagi-Sugeno (TS) fuzzy observer is designed to estimate actuator fault signals. To handle unmeasurable premise variables, a nonlinear partitioning method reconstructs the TS fuzzy model into an N-TS fuzzy form. Unlike conventional TS fuzzy models with linear consequents, this reformulation incorporates the differential mean value theorem to explicitly address unmeasured nonlinearities–a common difficulty in TS fuzzy observer design. Using Lyapunov stability theory, the fault estimator is formulated as an optimization problem subject to strict linear matrix inequalities (LMIs), which can be solved efficiently with numerical tools. The proposed observer is validated through co-simulations in Simulink and CarSim, demonstrating its potential for deployment in cloud computing environments to enhance fault management in AGVs.