Traditional parameter tuning of cascade controllers heavily depends on practical experiments and expert experience, which can be both time-consuming and complex. This study proposes a feedforward and cascade feedback PID control scheme to address vehicle trajectory-following challenges and develops a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for adaptive tuning of controller parameters. A range of scenarios and control strategies are simulated and evaluated through joint simulations in Matlab/Simulink and Carsim. The simulation results demonstrate that the proposed method effectively adjusts controller coefficients online through learning, reduces calibration time, and enhances trajectory-following performance and adaptability compared to alternative control approaches.
Adaptive Cascade Control for Vehicle Trajectory Following Based on Deep Reinforcement Learning
Lect. Notes Electrical Eng.
International Conference on Electric Vehicle and Vehicle Engineering ; 2024 ; Zhuhai, China December 20, 2024 - December 22, 2024
Selected Contributions of 2024 2nd International Conference on Electric Vehicle and Vehicle Engineering ; Chapter : 29 ; 273-278
2025-07-01
6 pages
Article/Chapter (Book)
Electronic Resource
English
Vehicle following speed control method based on deep reinforcement learning
European Patent Office | 2023
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