发表机构
RWTH Aachen University; University of Alberta（亚琛工业大学; 阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过行为克隆训练深度神经网络模仿模型预测控制，用于氢-柴油双燃料发动机的实时逐循环控制，在低成本硬件上实现快速推理并保持专家级性能。
AI 中文摘要
氢-柴油双燃料（H2DF）燃烧发动机为实现难以电气化的运输部门的脱碳提供了一条有前景的途径，然而其高度非线性的动力学和耦合的过程变量要求采用考虑约束的控制策略。模型预测控制（MPC）满足这些要求，但需要在每个燃烧循环中进行在线优化，这限制了其在低成本嵌入式硬件上的部署。本文通过行为克隆（BC）训练一个前馈深度神经网络（DNN）来模仿MPC专家，使用了在一台改装的康明斯QSB 4.5升氢双燃料发动机上以1500转/分钟收集的86,000个发动机循环的演示数据。两个变体，一个有过程反馈，一个没有，均进行了实验验证。两者在跟踪未见过的快速瞬态负载阶跃（3-8巴指示平均有效压力，IMEP）时，归一化均方根误差（NRMSE）分别为7.80%和9.03%，而专家的为8.01%，同时将平均氮氧化物和颗粒物排放保持在专家水平或以下。在树莓派400（ARM Cortex-A72，2.2 GHz）上，推理耗时不超过2毫秒，包括1毫秒的通信延迟，而MPC专家最多需要7毫秒，加速3.5倍。在180 MHz的低成本ESP32微控制器上进行开环性能分析，每次推理为4.3毫秒，比18毫秒循环窗口所需的时间快4倍。超出训练范围后，克隆的策略会饱和其控制量，但超过了压力升高率限制。据作者所知，这是首个用于内燃机（ICE）逐循环燃烧控制的实验性BC控制器，其训练数据来自发动机本身记录的演示。
英文摘要
Hydrogen-diesel dual-fuel (H2DF) combustion engines offer a promising pathway for decarbonising hard-to-electrify transport sectors, yet their highly nonlinear dynamics and coupled process variables demand constraint-aware control strategies. Model Predictive Control (MPC) meets these requirements but requires an online optimisation in every combustion cycle, which limits deployment on low-cost embedded hardware. This paper trains a feedforward deep neural network (DNN) by behaviour cloning (BC) to imitate an MPC expert, using 86,000 engine cycles of demonstration data collected at 1500 min-1 on a modified Cummins QSB 4.5-litre hydrogen dual-fuel engine. Two variants, one with process feedback and one without, are validated experimentally. Both track unseen fast-transient load steps (3-8 bar indicated mean effective pressure, IMEP) with normalised root mean square error (NRMSE) values of 7.80% and 9.03% against the expert's 8.01%, while keeping mean NOx and particulate matter emissions at or below those of the expert. Inference takes 2 ms or less on a Raspberry Pi 400 (ARM Cortex-A72 at 2.2 GHz), including 1 ms communication latency, compared to up to 7 ms for the MPC expert, a 3.5x speedup. Open-loop profiling on a low-cost ESP32 microcontroller at 180 MHz gives 4.3 ms per inference, 4x faster than required for the 18 ms cycle window. Beyond the training range the cloned policy saturates its controls but exceeds the pressure-rise-rate limit. To the authors' knowledge, this is the first experimental BC controller for cycle-to-cycle combustion control of an internal combustion engine (ICE), trained from demonstrations recorded on the engine itself.
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