Fault detection and isolation for unknown nonlinear systems using expert methods

In this paper a new comprehensive method for fault detection and isolation (FDI) for unknown nonlinear systems is presented. This method detects and eliminates sensor and actuator faults, as well as plant´s component faults. Fault type and location are precisely determined using sensor measurements and controller signals. Fault magnitude of sensor and actuator gain/bias faults is estimated using neuro-fuzzy models and gradient descent method. A fuzzy compensator with an adaptive output gain accommodates the faults and eliminates their effects for a wide range of plant´s components. Simulation results on a two-link rigid planar manipulator demonstrate the capability of the proposed technique for detection, diagnosis and accommodation of faults