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.

翻译：多燃料压缩点火（CI）发动机具有优越的功率密度和燃料灵活性。然而，在广泛的运行工况下实现一致且最优的燃烧相位仍是一项重大挑战，尤其是在存在建模不确定性的情况下。本文提出了一种新颖的、用于多燃料CI发动机燃烧控制的数据驱动实时不确定性补偿框架。所提出的方法引入了一种伪发动机转速，使得控制输入能够动态适应影响发动机的不确定性。为对底层燃烧过程进行建模，首先利用可获得的输入-输出数据训练一个高斯过程回归（GPR）模型，以捕捉不同运行工况下非线性和燃料依赖的行为。然后，通过已学得的GPR替代模型的模型反演来合成控制输入，并辅以一个不确定性补偿器，该补偿器旨在减轻由运行工况的动态变化和模型不准确性引起的偏差。这一集成控制策略允许在有限个燃烧循环内进行实时的输入修正。理论分析为所提出的控制器建立了有限时间收敛保证。仿真结果表明，所提出的方法能够实时地将燃烧相位引导至期望值，为多燃料CI发动机的运行提供了一种可扩展且自适应的控制解决方案。