Affiliations: [a]
School of Electrical & Automation Engineering, Hefei University of Technology, Hefei, PR China | [b] Faculty of Science, Engineering & Technology, Swinburne University of Technology, VIC 3122, Australia
Correspondence:
[*]
Corresponding author. Hai Wang, Professor (Huangshan Young Scholar) School of Electrical & Automation Engineering, Hefei University of Technology, Hefei 230009, PR China. Tel./Fax: +86 551 6290 1408; E-mails: haiwang_1229@hfut.edu.cn, wanghai0652@163.com.
Abstract: This study develops a novel vehicle stability control (VSC) scheme using adaptive neural network sliding mode control technique for Steer-by-Wire (SbW) equipped vehicles. The VSC scheme is designed in two stages, i.e., the upper and lower level control stages. An adaptive sliding mode yaw rate controller is first proposed as the upper one to design the compensated steering angle for enabling the actual yaw rate to closely follow the desired one. Then, in the implementation of the yaw control system, the developed steering controller consists of a nominal control and a terminal sliding mode compensator where a radial basis function neural network (RBFNN) is adopted to adaptively learn the uncertainty bound in the Lyapunov sense such that the actual front wheel steering angle can be driven to track the commanded angle in a finite time. The proposed novel stability control scheme possesses the following prominent superiorities over the existing ones: (i) No prior parameter information on the vehicle and tyre dynamics is required in stability control, which greatly reduces the complexity of the stability control structure. (ii) The robust stability control performance against parameter variations and road disturbances is obtained by means of ensuring the good tracking performance of yaw rate and steering angle and the strong robustness with respect to large and nonlinear system uncertainties. Simulation results are demonstrated to verify the superior control performance of the proposed VSC scheme.
Keywords: Finite time convergence, radial basis function neural network, robustness, steer-by-wire
