Robust Control and Actuator Fault Detection Based on an Iterative LMI Approach: Application on a Quadrotor

Abstract

Linear Matrix Inequalities (LMIs) have recently gained momentum due to the increasing performance of computing hardware. Many current research activities rely on the advantages of this growth in order to design controllers with provable stability and performance guarantees. To guarantee robustness despite actuator faults, model uncertainty, nonlinearities, and measurement noise, a novel iterative LMI approach is presented to design an observer-based state feedback controller allowing for simultaneous optimization of the control and observer gains. A comparison with a combination of an Extended Kalman Filter (EKF) and a Linear-Quadratic Regulator (LQR) has been conducted, inherently providing guaranteed stability for the closed loop only when the separation principle holds, which is not the case in this study. Both approaches are applied on a quadrotor, where reliable detection and compensation of the faults in the presence of measurement noise is demonstrated.