A new fault tolerant control system based on adaptive unscented Kalman filter and fuzzy decision making approach

In this work, a new fault tolerant control (FTC) methodology is proposed to deal with the potential problems due to possible fault scenarios. For this purpose, a state estimation scheme has been developed using an adaptive unscented Kalman filter (AUKF) approach. A fuzzy-based decision making (FDM) algorithm is introduced to diagnose sensor and/or actuator faults. The proposed fault detection and identification (FDI) approach is utilized to recursively correct the measurement vector and the model used for state estimation and prediction in the MPC formulation. The performance capabilities of the proposed FTC methodology is demonstrated by conducting series of simulation studies on a benchmark continuous stirred tank reactor (CSTR). Analysis of the simulation results reveals that the FTC scheme facilitates significant recovery in the closed loop performance particularly on occurrence of multiple sequential faults.