Knowledge of tire forces is important for vehicle control systems that aim to enhance vehicle handling and passenger safety. This paper introduces a nonlinear observer that estimates the coefficient of friction and tire slip angle, both of which are key factors in characterizing the vehicle's lateral tire forces. The observer estimates the friction coefficient and slip angle based on sensor measurements available on many production vehicles. This includes steering torque measurements readily available in steer-by-wire or electric power steering (EPS) systems. Experimental results on a steer-by-wire research vehicle (Fig. 1) demonstrate the observer's ability to provide accurate estimates out to the limits of handling.
This paper presents a method for designing robust, model-based fault detection filters for linear systems with bounded parametric uncertainty. The approach maximizes the theoretical channel capacity of the system and diagnostic filter together, regarding the fault condition as an input to a hypothetical communication channel and the resulting residual as the output, with sensor noise, disturbances, and system input as sources of interference. This results in a robust residual that is both sensitive to faults and insensitive to noise and disturbances. The effectiveness of the technique is demonstrated in the design of a fault detection filter for use with a diagnostic system for a steer-by-wire vehicle.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.