Data-driven Control with Real-time Uncertainty Compensation for Multi-Fuel Engines
2026-06-15 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new way to control engines that can run on different fuels, helping them work better and more reliably under changing conditions. They use a data-driven model called Gaussian Process Regression to predict how the engine behaves with different fuels and operating settings. Then, they adjust control inputs in real time, correcting for uncertainties and changes to keep the engine running smoothly. Their method is proven to quickly find the right settings and works well in simulations.
Compression Ignition EngineMulti-fuel EngineCombustion PhasingGaussian Process RegressionModel InversionReal-time ControlUncertainty CompensationDynamic AdaptationControl Theory
Authors
Rajasree Sarkar, Arunava Banerjee, Sathya Aswath Govind Raju, Ishan Berk Altiner, Zongxuan Sun, Kenneth Kim, Chol-Bum Mike Keown
Abstract
Multi-fuel compression ignition (CI) engines offer superior power density and fuel flexibility. However, achieving consistent and optimal combustion phasing across a wide range of operating conditions remains a major challenge, particularly in the presence of modeling uncertainties. This paper presents a novel, data-driven real-time uncertainty compensation framework for combustion control in multi-fuel CI engines. The proposed approach introduces a pseudo-engine speed that enables dynamic adaptation of control inputs in response to uncertainty affecting the engine. To model the underlying combustion process, a Gaussian Process Regression (GPR) model is first trained on available input-output data, capturing the nonlinear and fuel-dependent behavior across varying operating conditions. Control inputs are then synthesized through model inversion of the learned GPR surrogate and augmented with an uncertainty compensator designed to mitigate deviations caused by dynamic variations in operating conditions and model inaccuracies. This integrated control strategy allows for real-time input corrections within a finite number of combustion cycles. Theoretical analysis establishes finite-time convergence guarantees for the proposed controller. Simulation results demonstrate that the proposed method steers the combustion phasing to the desired value in real-time, providing a scalable and adaptive control solution for multi-fuel CI engine operation.