Abstract
A steer-by-wire (SbW) system, also known as a next-generation steering system, is one of the core elements of autonomous driving technology. Navigating a SbW system road vehicle in varying driving conditions requires an adaptive and robust control scheme to effectively compensate for the uncertain parameter variations and external disturbances. Therefore, this article proposed an adaptive global fast sliding mode control (AGFSMC) for SbW system vehicles with unknown steering parameters. First, the cooperative adaptive sliding mode observer (ASMO) and Kalman filter (KF) are established to simultaneously estimate the vehicle states and cornering stiffness coefficients. Second, based on the best set of estimated dynamics, the AGFSMC is designed to stabilize the impact of nonlinear tire-road disturbance forces and at the same time to estimate the uncertain SbW system parameters. Due to the robust nature of the proposed scheme, it can not only handle the tire–road variation, but also intelligently adapts to the different driving conditions and ensures that the tracking error and the sliding surface converge asymptotically to zero in a finite time. Finally, simulation results and comparative study with other control techniques validate the excellent performance of the proposed scheme.
Highlights
The automobile industry is immensely working to transform conventional road vehicles into partial/full autonomous vehicles
The estimation performance of the adaptive sliding mode observer (ASMO) for lateral velocity and yaw rate primarily depends upon the knowledge of tire cornering stiffness coefficients C f and Cr, which are unknown in practice and cannot be measured directly from the onboard vehicle sensors
To tackle the residual disturbance left by the estimated dynamics-based control (EDC) and estimate the uncertain steering parameters, the adaptive global fast sliding mode control (AGFSMC) u A is designed as follows:
Summary
The automobile industry is immensely working to transform conventional road vehicles into partial/full autonomous vehicles. In [23,24], adaptive control is implemented for path tracking via SbW system and the authors estimated the sliding gains by considering the known steering parameters and cornering coefficients. They did not use any mechanism to stop the estimation. We have established the adaptive sliding mode observer (ASMO) and the Kalman filter (KF) to simultaneously estimate the vehicle states and cornering stiffness coefficients by using the yaw rate and the strap down [35] lateral acceleration signals. 6 describes the simulation followed by the last section that concludes the paper
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