An Adaptive PI Controller by Particle Swarm Optimization for Angle Tracking of Steer-by-Wire

In this article, a novel steer-by-wire system architecture called iSteer is designed to realize the high performance of steering angle tracking for self-driving vehicles (especially, self-driving trucks), where the vehicle steers in response to the virtual driver's command instead of the driver's hand wheel. Based on a newly steering dynamical model of the iSteer system,an adaptive proportionalintegral controller with parameters tuning by particle swarm optimization is developed for real-time or embedded application. Hence, the fast and efficient computation plays an essential role in our control law design. The developed steering controller not only meets this computational demand but also takes into account the self-aligning torque and ground resistance torque (as the disturbances to the iSteer control system). According to the system transfer function, the one-step prediction of steering angle is utilized to find the best particle of the swarm. Through a graph analysis using Nyquist stability criterion, we prove that the steering control system is stable and the error of steering angle tracking is convergent. Some experiments are tested on the physical iSteer system with the developed steering controller, the results of which further verify the effectiveness of the designed architecture and the high performance (e.g., high precision in tracking and fast response time) of the developed control law for steering angle tracking in self-driving vehicle without hand wheel.

Automated summary

In this article, a novel steer-by-wire system architecture called iSteer is designed to achieve high performance steering angle tracking for self-driving vehicles. An adaptive proportional-integral controller is developed with parameters tuning by particle swarm optimization. The control law takes into account self-aligning torque and ground resistance torque. The stability and convergence of the steering control system are proven through analysis and experiments.