Autonomous vehicle trajectory tracking lateral control based on the terminal sliding mode control with radial basis function neural network and fuzzy logic algorithm

Abstract

Abstract. This paper will study a trajectory tracking control algorithm for electric vehicles based on a terminal sliding mode controller. First, a 3 degrees of freedom nonlinear vehicle model and a controller-oriented 2 degrees of freedom vehicle model are established. The preview time is adaptively adjusted based on the preview model. Then, the vehicle trajectory tracking controller, which uses the terminal sliding mode algorithm, is designed. The radial basis function (RBF) neural network algorithm is used to approximate the system variable parameters in the control model online. At the same time, fuzzy logic is used to control the gain parameters of the controller to reduce the chattering of the control system. Finally, the designed controller is verified by simulation. The maximum deviation of path tracking under different speeds is 0.6 m, and the target path can also be well followed under different road friction coefficients. The simulation results show that the controller designed in this paper can effectively carry out the vehicle trajectory tracking and lateral control and reduce the chattering to a certain extent.

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system

“…For example, when k = 300s, the curves corresponding to e m = 0.9 and e m = 1.1 approximately overshoot the true value by 0.4 and 0.3 rad, respectively. The reasons for this phenomenon are as follows: if the selection value is larger than 1, a static output difference will exist in system (28). If the selection value is smaller than 1, the purpose of suppressing the measurement disturbance cannot be achieved.…”

“…If the selection value is smaller than 1, the purpose of suppressing the measurement disturbance cannot be achieved. Therefore, for system (28), e m = 1 yields better angular tracking performance.…”

“…Jiang and Astolfi 26 suggested backstepping and forwarding control designs to address the underactuated issues of a class of nonlinear, underacted systems concerning nonlinear lateral dynamics control. Fan et al and Wang et al 27,28 developed the sliding mode controller of the radial basis function neural network to address the lack of robustness of highly nonlinear vehicle models. Tang et al 29 effectively proposed an improved artificial electric field algorithm (I-AEFA) and creatively applied it to robot path planning.…”

A trajectory tracking control method for autonomous vehicle based on adaptive model predictive control

“…In order to enable vehicles to respond precisely and rapidly to complex road conditions, numerous scholars both domestically and internationally have conducted extensive research on trajectory tracking control for autonomous vehicles. The main control algorithms employed include pure pursuit control [2] , PID control [3] , fuzzy control [4] , LQR control [5] , Model Predictive Control (MPC) [6] , and reinforcement learning control [7] . Compared to other control algorithms, MPC control has been widely applied in the research field of autonomous vehicle trajectory tracking control due to its advantages in handling multiple constraints and variables, foreseeing future vehicle states, and exhibiting strong robustness [8][9] .…”