Actuator Fault Detection in Nonlinear Uncertain Systems Using Neural On-line Approximation Models

This paper presents a methodology for actuator fault detection in unknown, input-affine, nonlinear systems using neural networks. Both state feedback and output feedback cases are considered. Neural net tuning algorithms are derived and fault identifiers are developed using the Lyapunov approach. The paper studies properties of the fault dynamics, the dynamics of a fault evolution process. The actuator-fault dynamics are analyzed and a rigorous detectability condition is given for actuator faults relating the actuator desired input signal, neural net-based observer sensitivity, and detectability time. Moreover, the issue of fault propagation through the system dynamics towards the measurable output is addressed and specific conditions under which such faults can be detected are proposed. Simulation results are presented to illustrate the effectiveness of the proposed technique.