Fault detection, diagnosis and accommodation of dynamical systems with actuator failures via online approximators

A general framework for the detection, diagnosis and accommodation of a class of actuator faults is developed. This framework, which addresses both abrupt and incipient faults, utilizes a nonlinear adaptive detection observer along with an online approximation scheme for failure assessment. The actuator failure is modeled by an additive perturbation of the actuator signal (actuator gain) that is described by a nonlinear function of both the input and output signals. Robust adaptive schemes are introduced to account for modeling errors that affect the diagnosis process and may cause false alarms. Numerical studies for a second-order system using a neural network-based online approximator are presented to demonstrate the applicability of the proposed automated diagnostic scheme.