Disturbance adaptive steering wheel torque control for enhanced path tracking of autonomous vehicles

This paper presents a disturbance adaptive steering wheel torque control for enhanced path tracking of autonomous vehicles. A lateral controller in automated vehicle consists of high-level controller, which generates a desired path, and low-level controller, which controls motor torque in a steering system for tracking the desired path. In order to ensure robustness against uncertainty in the steering system, it is important to estimate the disturbance such as rack force from tire forces, and friction forces. The disturbance estimation considers the simplest random walk model, which treats disturbance as a random constant. The proposed estimation uses simplified steering system, which consists of moment of inertia, damping coefficient, friction force, and disturbance. In order to track desired path from high-level controller, adaptive sliding mode control (ASMC) has been applied using motor torque input in an electric-power-steering (EPS) system. The proposed the disturbance estimation for path tracking algorithm are evaluated via computer simulation results. Test results show the proposed steering control algorithm achieved satisfactory tracking performance.