Model based health monitoring of vehicle steering system using sliding mode observers

This paper presents a solution to the problem of diagnosing faults in a vehicle steering system using nonlinear observers constructed by sliding mode observer design techniques. A bicycle vehicle model using small angle approximations is used to represent vehicle behavior. The model reproduces the in plane motions (longitudinal velocity, lateral velocity and yaw rate) of the vehicle. A bank of observers each utilizing different measurements to estimate the system states are constructed using sliding mode theory. The sliding observers are used in a binary phase detection filter configuration to isolate sensor faults. Further the design of the sliding observers is extended to estimate the inputs acting on the system and these estimates are then used for isolation of actuator faults. The effectiveness of the diagnostic scheme is demonstrated through the use of simulations. Further, experimental implementation currently being scheduled in collaboration with the Transportation Research Center of Ohio.