Deep Behaviour Cloning of Model Predictive Control for Real-Time Operation of a Hydrogen-Diesel Dual-Fuel Engine

By Winkler, Mana, Gordon +1Sep 27, 2026

Score████░░░░░░4.3

Key numbers

3.5x speedup
4x faster than required for
7.80% and 9.03% against the

VerdictWorth a reader's time today.

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Abstract

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.

Alexander Winkler, Neeraj Naduvath Mana, David Gordon, Jakob Andert

The editor's rubric

Heuristic review

Dimension	Level	Weight	What that level means
Leverage	██░░░ 2	16%	Reusable within one subfield (a technique, dataset, or protocol a few groups will adopt).
Magnitude	███░░ 3	20%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░ 3	20%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	10%	A new combination of known ideas.
Trajectory	███░░ 3	14%	A clear path to scale.
Stakes	███░░ 3	20%	Meaningful benefit to many people within a few years.

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: gains (x-fold, outperforms); verification (held-out test, experimental validation); scale (efficient, low cost); stakes (energy, climate).

How the score was computed

rank-2026-09-29

Score████░░░░░░4.3

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Merit: 5.5 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.7 / 10; Shrunk toward the desk prior by editor confidence (46%).
Attention: 0%; Citations, upvotes, points, mentions.
Freshness: 83%; Half-life decay since publication.

Citations0 (reference 15, via semantic-scholar, Sep 29, 2026, 23:37 UTC)

The record

Reviewed by heuristic-v2 on Sep 29, 2026, 23:53 UTC. Paper type: empirical.
Categories: eess.SY
BRIEF, No.3 in the Climate & Energy edition of September 30, 2026.
TOP, No.2 in the Climate & Energy edition of September 29, 2026.