Time-Delay Deep Q-Network Based Retarder Torque Tracking Control Framework for Heavy-Duty Vehicles

Abstract

The stability of brake control is an important guarantee for the safety of heavy-duty vehicles (HDVs) at high speeds. However, the electro-hydraulic actuation braking systems often exhibit a significant delay in seconds, which makes braking performance forecasting and control difficult. To address the torque tracking control problem with time delay, a deep inference and control method is proposed. First, a theoretical delay time under different rotating speeds is identified with a data-driven model. Then, a fast end-to-end prediction model is established to estimate the torque performance of the next step with delay information. The deep Q-network (DQN) learning approach is proposed to learn the experimental data by exploring and seeking the optimal control strategy in the time delay environment. A comparative simulation of the proposed DQN-based controller with or without considering time delay, and the rule-based method with or without considering time delay is implemented, and an online processor-in-the-loop (PIL) test with the edge computing device NVIDIA Jetson Xavier NX is performed on the robustness condition. The simulation results and PIL test results demonstrate that the proposed control framework achieves a great improvement in the torque tracking effect with time efficiency.