Accommodation of partial actuator faults using output feedback based adaptive robust control

This work considers the problem of partial actuator fault accommodation. The faulty actuators are characterized by a loss in efficiency, which can potentially lead to system instability or degraded system performance. Conventional model reference adaptive control has been used to solve this problem in the literature, which provides a natural setting to solve such problems by virtue of its online learning capabilities. Unfortunately, such techniques can lead to unbounded states when output disturbances and other modeling uncertainties are considered. In the present work, we make the problem more practical by explicitly taking into account these factors. Furthermore, we assume that faults occur at unknown instants of time and only limited state information is available. For solving this problem, we develop an output feedback adaptive robust control (ARC) based technique, which can effectively deal with all the constraints and uncertainties present in the system. The technique combines the output feedback based adaptive backstepping with robust control techniques and uses discontinuous projection to ensure the unknown parameter estimates stay within a known convex set. Comparative simulation studies are performed on a linearized Boeing 747 model to evaluate performance of the proposed scheme versus conventional MRAC based technique.