Data-driven Control with Real-time Uncertainty Compensation for Multi-Fuel Engines

2026-06-14

AI Summary

This study addresses the challenge of maintaining optimal combustion phasing across a wide range of operating conditions in multi-fuel compression-ignition engines, where significant modeling uncertainties hinder precise control. To overcome this limitation, the authors propose a data-driven, real-time adaptive control framework that innovatively incorporates a pseudo-engine-speed mechanism. The approach synergistically combines Gaussian process regression with a finite-time convergent uncertainty compensator to enable online correction of fuel-dependent nonlinear combustion dynamics. Simulation results demonstrate that the proposed method can accurately regulate combustion phasing to the desired target within a limited number of engine cycles, exhibiting both strong adaptability to varying fuels and favorable scalability across diverse operating regimes.

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.

Problem

• multi-fuel engines
• combustion phasing
• modeling uncertainties
• real-time control
• compression ignition

Innovation

• data-driven control
• uncertainty compensation
• Gaussian Process Regression
• multi-fuel CI engine
• real-time adaptation