Abstract
A novel hierarchical heavy-duty vehicle (HDV) longitudinal control strategy with a hydraulic retarder is proposed in this paper to achieve the HDV down-hill longitudinal automation in the deceleration process. The upper level controller generates the optimal desired retarder torque through the H infinity control and the off-policy reinforcement learning (RL), in which the H infinity control is able to attenuate those disturbances and the off-policy RL can solve the H infinity control with completely unknown system dynamics. Then, according to the optimal desired retarder torque, the lower level controller can calculate the desired control pressure for the retarder to control the HDV. The effectiveness of this HDV longitudinal control strategy is verified by simulations based on an experimentally verified retarder model. Compared to the sliding-mode-control (SMC) based controller, the simulation result shows the proposed control strategy has better capability to attenuate the disturbances and guarantee the longitudinal speed tracking performance.
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