Deep truck cruise control: Field experiments and validation of heavy duty truck cruise control using deep reinforcement learning. (April 2022)

• Record Type: Journal Article
• Authors: Albeaik, Saleh; Wu, Trevor; Vurimi, Ganeshnikhil; Chou, Fang-Chieh; Lu, Xiao-Yun; Bayen, Alexandre M.
• Is Part Of: Control engineering practice. Volume 121(2022)
• Publication Date: 2022-04
• Subjects: 0000 -- 1111; Model-free deep reinforcement learning -- Cruise control -- Control validation -- Field experiments -- Heavy duty trucks; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.105026
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Building control systems for heavy duty trucks have historically been dependent on availability of the details of the mechanical configuration of each target truck. This article investigates transfer and robustness of continuous control systems learned using model free deep-RL as an alternative; a configuration agnostic strategy for control system development. For this purpose, deep-RL cruise control policies are developed and validated in simulation and field experiments using two differently configured trucks; full-size Volvo and Freightliner trucks. Their performance are validated for step, ramp, and sinusoidal reference speed trajectories to stimulate steady-state and transient behavior, and to test speed-tracking for low, high, and variable accelerations. The robustness of these controllers were validated for unmodeled gravity effects and for operating the controllers outside of the engine command training distribution bounds. In addition, the controllers were validated against a classical model-based controller. Highlights: Deep-only approaches potentially simplify and improve development of truck controls. Deep controllers successfully trained offline and its transfer validated in the field. A single model and control structure successfully employed to control two trucks.