Robust enhanced dual estimator design for fault and disturbance compensation in networked suspension systems under cyber attacks

This research addresses the challenge of designing a robust accommodation controller for fuzzy-based networked active suspension systems operating under faults, disturbances, and cyber attacks. A stochastic faulty dual-intermediate estimator framework is proposed to achieve this goal. The key advantages of the proposed estimator include its ability to handle stochastic failures within the estimator structure, tolerate unbounded faults and external disturbances, and eliminate the requirement for matching conditions. The primary objective is to minimize steady-state error, reduce overshoot, and accelerate convergence in order to enhance the performance of the fault and disturbance estimators. This is achieved by embedding a new dynamic subsystem with an intrinsic proportional integral structure into the intermediate estimator. The proportional integral-based design introduces tunable gains, which can be optimized to achieve superior performance in both transient and steady-state conditions. Using the linear matrix inequality framework and Lyapunov stability theory, it is proven that the estimation error states are uniformly ultimately bounded. A convex optimization problem is formulated and solved to simultaneously determine the gain matrices of both the controller and intermediate estimators. Finally, simulation results are presented to validate the effectiveness of the proposed strategy. Comparative analysis with the traditional intermediate estimator method further highlights the strengths and potential of the proposed approach.