Set membership fault detection for nonlinear dynamic systems

In this chapter, an innovative approach to fault detection for nonlinear dynamic systems is proposed, based on the recently introduced quasi-local set membership- identification method, overcoming some relevant issues proper of the “classical” techniques. The approach is based on the direct identification from experimental data of a suitable filter and related uncertainty bounds. These bounds are used to detect when a change (e.g., a fault) has occurred in the dynamics of the system of inter- est. The main advantage of the approach compared to the existing methods is that it avoids the utilization of complex modeling and filter design procedures, since the filter/observer is directly designed from data. Other advantages are that the approach does not require to choose any threshold (as typically done in many “classical” tech- niques), and it is not affected by under-modeling problems. An experimental study regarding fault detection for a drone actuator is finally presented to demonstrate the effectiveness of the proposed approach.