In this article, based on a novel electro-hydraulic braking system for full self-driving vehicles, a model predictive control approach is designed for precisely tracking of braking pressure. Concerning electric motor, reduction mechanism, and hydraulic system, a new braking system model is formulated. Due to the nonlinear hydraulic characteristic of the master cylinder piston, a quadratic polynomial is utilized to fit a relationship between piston position and cylinder pressure, by which a desired pressure of master cylinder is transformed into a desired position of the piston. A recursive least square with forgetting factor is developed to find the optimal parameters of pressure–position relationship. A system friction model is established to calculate the equivalent friction torque, which is regarded as the compensation of the desired torque. A vehicle test bench of full self-driving is established to validate the designed approach. According to the accurate tracking and the quick response of braking pressure from experimental results, it can be concluded that the model predictive control approach is a good candidate for the pressure demand control of electro-hydraulic braking by wire.

In this article, based on the estimation of optimal slip ratio, a model predictive control approach is proposed for the antilock braking system with motor of a battery electric vehicle. A wheel dynamics model with a virtual control quantity is established. A method for estimating the optimal slip ratio of different roads is introduced, which consists of a sliding mode observer for longitudinal braking force and a recognizer of slip ratio. A ground slip map that describes the relationship between slip ratio and ground adhesion coefficient is proposed, which is utilized to identify the optimal slip ratio. Based on the model predictive control algorithm, a braking torque controller is designed to track the optimal slip ratio for good performance of braking stability and security. The vehicle control unit embedded with the estimator and model predictive control law is tested in a battery electric vehicle on the dry and wet roads. The experimental results indicate that the proposed approach has a good performance for the estimation and control of slip ratio during the antilock braking with electric motor, especially suitable for the low adhesion road conditions.

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 proportional-integral 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.