The studies on the autonomous electric vehicle are quite attractive due to fewer human-induced errors and improved safety in recent years. Extensive research has been done on the autonomous steering control of the mobile robot, but study on the on-road autonomous electric vehicle is still limited. This paper proposes a potential field method to achieve the trajectory control of the autonomous electric vehicle with in-wheel motors. Instead of strictly following a desired path, this method can form a steering corridor with a desired tracking error tolerance and the vehicle can be steered smoothly with less control effort. In this paper, the innovative potential filed function is presented first to determine the desired vehicle yaw angle. Then, according to this desired yaw angle, a two-level trajectory controller is proposed to achieve the trajectory control. Simulation results are shown to prove that this suggested trajectory controller can successfully control the vehicle to move within the desired road boundary and improve the handling and stability performance of the vehicle.

In order to improve the lateral and directional performance of vehicles, this paper proposes a novel rear-steering based decentralized control (RDC) algorithm for four-wheel-steering (4WS) vehicles. Based on a linearized single-track vehicle model, a prototype of the RDC algorithm for linear conditions is derived. Linear analysis in frequency domain shows that the RDC can significantly reduce the magnitude and increase the phase lag of sideslip response as well as enlarge the bandwidth of yaw response in a broad frequency range. Through the incorporation of a novel sliding mode controller, the RDC algorithm is further extended for nonlinear applications. Analysis based on nonlinear model shows that, the RDC can guarantee the performance of the closed-loop (CL) system in the presence of uncertainties in system parameters and state feedback values. Moreover, the RDC is robust to arbitrary lateral disturbances and can guarantee that the vehicle converges to reference yaw rate and zero sideslip. Simulation results show that the RDC can effectively improve the lateral and directional performance of the vehicle in comparison with previous control methods. Excellent robustness to external disturbance and road friction variation is reported as well.

This paper qualitatively and quantitatively reviews and compares three typical tyre–road friction coefficient estimation methods, which are the slip slope method, individual tyre force estimation method and extended Kalman filter method, and then presents a new cost-effective tyre–road friction coefficient estimation method. Based on the qualitative analysis and the numerical comparisons, it is found that all of the three typical methods can successfully estimate the tyre force and friction coefficient in most of the test conditions, but the estimation performance is compromised for some of the methods during different simulation scenarios. In addition, all of these three methods need global positioning system (GPS) to measure the absolute velocity of a vehicle. To overcome the above-mentioned problem, a novel cost-effective estimation method is proposed in this paper. This method requires only the inputs of wheel angular velocity, traction/brake torque and longitudinal acceleration, which are all easy to be measured using available sensors installed in passenger vehicles. By using this method, the vehicle absolute velocity and slip ratio can be estimated by an improved nonlinear observer without using GPS, and the friction force and tyre–road friction coefficient can be obtained from the estimated vehicle velocity and slip ratio. Simulations are used to validate the effectiveness of the proposed estimation method.