Fault Detection using a Reduced Order Filter for Active Suspension Systems Represented by Takagi-Sugeno Fuzzy Models

This paper proposes a reduced-order fault detection filter based on Takagi-Sugeno (T-S) modeling and $H_{\infty}$ analysis, guaranteeing robustness and accuracy in the face of disturbances. Simulations show that the strategy we have developed provides effective monitoring of the active suspension system, while maintaining a good compromise between robustness, accuracy and low computational complexity. This study paves the way for the integration of advanced fault detection techniques to improve the safety of intelligent and autonomous vehicles.