Methods for modeling the steering wheel torque of a steer-by-wire vehicle

Unlike electromechanical steering systems, steer-by-wire systems do not have a mechanical coupling between the wheels and the steering wheel. Therefore, a synthetic steering feel has to be generated to supply the driver with the necessary haptic information. In this paper, the authors analyze two approaches of creating a realistic steering feel. One is a modular approach that uses several measured and estimated input signals to model a steering wheel torque via mathematical functions. The other approach is based on an artificial neural network. It depends on steering and vehicle measurements. Both concepts are optimized and trained, respectively, to best fit a reference steering feel obtained from vehicle measurements. To carry out the analysis, the two approaches are evaluated using a simulation model consisting of a vehicle, a rack actuator, and a steering wheel actuator. The research shows that both concepts are able to adequately model a desired steering feel.

Fuzzy-Based Adaptive Higher-Order Sliding Mode Control for Uncertain Steer-by-Wire System

This paper proposes an adaptive higher-order sliding mode (AHOSM) control method based on the adaptive fuzzy logic system for steer-by-wire (SbW) system to achieve the tracking control of the front wheels steering angle. First, an adaptive fuzzy logic system is adopted to estimate the unknown dynamics of the SbW system. Then, the AHOSM control is constructed to overcome the lumped uncertainties including unknown external perturbation and fuzzy logic system approximation error, and has the advantage of attenuating the chattering caused by the discontinuous control signal. Finally, the adaptation scheme is designed for the dynamic gain of the proposed AHOSM controller without a priori knowledge of the bounds of the uncertainties. In contrast to the existing controllers applied in the SbW system, this controller has a better control performance in practical application. By means of Lyapunov stability analysis, it is theoretically proved that the system trajectory converges to an adjustable neighborhood of the origin in finite time. Simulations and vehicle experiments are carried out to verify the effectiveness of the proposed approach.