Design of Adaptive Trajectory-Tracking Controller for Obstacle Avoidance and Re-Planning
Year: 2025
Type: article
Abstract: In order to solve problems of poor stability and large trajectory-tracking errors when intelligent vehicles are travelling at different speeds, when working conditions that require obstacle avoidance are not taken into account in the trajectory-tracking process, an obstacle avoidance re-planner adaptive trajectory-tracking controller is proposed. For this obstacle avoidance trajectory re-planner, the prediction model is calculated based on the vehicle point mass model, an objective function utilizing the obstacle avoidance function is designed, and finally the obstacle avoidance trajectory is output using a fifth-degree polynomial fitting. For the trajectory-tracking controller, 138 sets of valid data are screened from 300 sets of offline simulation experiments, and the optimal combinations of different vehicle travel speeds and predicted time domains are obtained using grey correlation analysis, and each set of speeds and predicted time domains are fitted using Fourier approximation to design the adaptive parameter model. Using CarSim/Simulink co-simulation, simulation results comparing obstacle avoidance performance and trajectory-tracking performance between the fixed time-domain controller and the controller designed in this paper show that the control accuracy of the controller designed in this paper is improved by 19.9% and the solution speed is increased by 15% at 50 km/h speed; at 100 km/h speed, the maximum traverse angle deviation and maximum lateral deviation are reduced by 0.5% and 26.9%, respectively. In the multi-obstacle environment, the controller is able to achieve obstacle avoidance, and the lateral deviation, traverse angular velocity, and centre-of-mass lateral deviation are all better than those of the fixed time-domain controller. It can be seen that the controller designed in this paper is more stable and has better tracking performance when considering obstacle avoidance.
Source: World Electric Vehicle Journal
DOI: 10.3390/wevj16040191
Authors: Zihao Kang, Changshui Wu
Institution: Shanghai University of Engineering Science
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,689
OpenAlex ID: W4408781834