A novel fault reconstruction and estimation approach for a class of systems subject to actuator and sensor faults under relaxed assumptions.

In this paper, we propose a new fault reconstruction and estimation (FRE) scheme for a class of nonlinear systems subject to both actuator and sensor faults, under relaxed assumptions. Indeed, in our approach, we assume that the total number of actuator and sensor faults is greater than the number of outputs, we consider a more relaxed rank matching condition and we relax the classical minimum phase assumption, which enlarges considerably the class of systems and applications for which our approach may be addressed compared to existing methods in the literature. After augmenting the system by the dynamics of filtered outputs, we generate auxiliary outputs until the observer matching condition with respect to actuator faults vector becomes satisfied. Next, a new high gain sliding mode observer is designed for the system of auxiliary outputs to estimate both auxiliary states and sensor faults. The estimates of auxiliary outputs and sensor faults are then used by an unknown input observer (UIO) whose the objective is to reconstruct the states of the considered nonlinear system. Finally, we show that we can reconstruct the actuator faults by exploiting the dynamics of auxiliary outputs and using the estimates of system states and sensor faults. Theoretical results are established based on Lyapunov analysis and sliding modes theory. Numerical simulations are applied to a single link robot system and a steer-by-wire vehicle under disturbances and noise to validate theoretical results and to illustrate the good performances of the proposed fault reconstruction and estimation scheme.