Shifting strategy for power shift tractors based on digital Twin-Driven reinforcement learning

Abstract

To address the unstable power output and low operational efficiency in high-power power-shift tractors (PST) caused by engine performance variations and traction resistance fluctuations, this study proposes a power-shift strategy based on reinforcement learning and digital twin technology. A digital twin system is developed to achieve real-time synchronization between the physical PST and its virtual model through multi-source sensor data acquisition and standardized signal processing, enabling bidirectional interaction and dynamic environment simulation. The proposed strategy integrates a twin-delayed deep deterministic policy gradient (TD3) reinforcement learning framework to mitigate Q-value overestimation and enable adaptive optimization of shifting decisions under complex operating conditions. Compared with traditional optimization methods such as dynamic programming and conventional neural networks, the TD3-based approach demonstrates superior adaptability and control stability, particularly in maintaining smooth shifting and continuous power delivery under varying load conditions. Furthermore, to address throttle fluctuation during gear transitions, a fuzzy PID throttle controller is introduced, which dynamically adjusts PID gains based on real-time throttle deviation and its rate of change. Experimental results show that the proposed method significantly reduces vehicle speed tracking errors and fuel consumption while improving gear-shift smoothness. Specifically, the mean engine torque and fuel consumption tracking errors remain below 6.11 N·m and 1.86 g·(kW·h)^–1, respectively. Compared to traditional strategies, the method achieves a lower mean speed tracking error (0.0121 m·s^–1), fuel consumption rate (231.21 g·(kW·h) ^–1), and total number of shifts (39).This study presents an effective and intelligent gear-shifting solution for PSTs and offers valuable insights for the broader application of reinforcement learning in agricultural machinery control.