Affiliations: [a] College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| [b] Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC, Australia
Correspondence:
[*]
Corresponding author. Zhe Sun, College of Information Engineering, Zhejiang University of Technology,
288 Liuhe Road, Hangzhou, Zhejiang 310023, China. E-mail: [email protected].
Abstract: Due to the complex driving conditions confronted by an autonomous vehicle, it is significant for the vehicle to possess a robust control system to achieve effective collision-avoidance performance. This paper proposes a neural network-based adaptive integral terminal sliding mode (NNAITSM) control scheme for the collision-avoidance steering control of an autonomous vehicle. In order to describe the vehicle’s lateral dynamics and path tracking characteristics, a two-degrees-of-freedom (2DOF) dynamic model and a kinematic model are adopted. Then, an NNAITSM controller is designed, where a radial basis function neural network (RBFNN) scheme is utilized to online approximate the optimal upper bound of lumped system uncertainties such that prior knowledge about the uncertainties is not required. The stability of the control system is proved via Lyapunov, and the selection guideline of control parameters is provided. Last, Matlab-Carsim co-simulations are executed to test the performance of the designed controller under different road conditions and vehicle velocities. Simulation results show that compared with conventional sliding mode (CSM) and nonsingular terminal sliding mode (NTSM) control, the proposed NNAITSM control scheme owns evident superiority in not only higher tracking precision but also stronger robustness against various road surfaces and vehicle velocities.
Keywords: Autonomous vehicle, neural networks, sliding mode control, vehicle dynamics and control
