Abstract
Longitudinal dynamics control is the basis for autonomous driving of intelligent vehicles, which have great significance to the development of intelligent transportation system (ITS). To solve the problems of traditional sliding mode control method when applied to intelligent vehicle longitudinal dynamics, such as large velocity tracking errors, strong chattering phenomenon and so on, a new sliding mode control strategy based on RBF (Radical Basis Function) neural network is presented in this paper. Firstly, a nonlinear mathematical model of the intelligent vehicle longitudinal motion is established by considering the dynamics of the engine, the torque converter, the automatic transmission and the brake system. On the basis of the system model, a variable structure control system with sliding mode is introduced to design a sliding mode variable controller with RBF neural network. This controller can adaptively adjust the switching gain and its stability is proved based on the Lyapunov theory. Finally, the effectiveness of the designed longitudinal velocity control strategy is verified by simulation under typical driving conditions. The simulation results show that the improved control algorithm can effectively suppress chattering, obtain the higher precision and stronger robustness than the traditional sliding mode control. Thus, the longitudinal motion control performance of intelligent vehicles is improved effectively.
Highlights
As the development direction of future vehicles and the central part of intelligent transportation system (ITS), intelligent vehicles have been widely concerned by scholars from different countries in recent years [1], [2], [26]
Ferrara et al to design the second-order sliding mode longitudinal control strategy based on least sensors, with an acceleration observer constructed to estimate the vehicle acceleration [4]
A non-singular terminal sliding mode control strategy based on Radial Basis Function (RBF) (Radical Basis Function) neural network is proposed to regulate longitudinal velocity in this paper
Summary
As the development direction of future vehicles and the central part of intelligent transportation system (ITS), intelligent vehicles have been widely concerned by scholars from different countries in recent years [1], [2], [26]. Intelligent vehicles usually can complete one or more driving tasks such as road identification and tracking, obstacle recognition and collision avoidance, vehicle detection and tracking, vehicle lateral and longitudinal motion control, etc. Longitudinal control is the basis of autonomous driving of intelligent vehicle and the main content to realize the active safety of. With the development of intelligent vehicles and ITS, many scholars have conducted in-depth research on the vehicle longitudinal control system. Ferrara et al to design the second-order sliding mode longitudinal control strategy based on least sensors, with an acceleration observer constructed to estimate the vehicle acceleration [4]. Peng designed a vertical adaptive inversion control strategy for intelligent vehicles, which had strong robustness to guarantee the stability of the closed-loop system [7]. Palhares et al used the system identification method to establish the longitudinal dynamic model and compensated
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
