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<p>Robust control of natural-gas engines under unknown load disturbances remains challenging due to strong couplings and delays in multi-input multi-output (MIMO) dynamics. This paper presents a control framework that integrates rate-based model predictive control (MPC) with a gain-scheduling scheme driven by an adaptive Kalman filter to enhance performance under unknown load disturbances. A novel adaptation mechanism enables the Kalman filter to rapidly track transient changes in load torque while attenuating steady-state estimation noise. The online torque estimate is used to compute local equilibrium operating points and generate a gain-scheduling parameter matrix that adaptively adjusts MPC behavior to improve transient response. Experimental validation on a laboratory engine demonstrates that the estimator converges quickly during load transients and maintains low steady-state noise; when combined with gain-scheduled MPC, the proposed controller significantly reduces speed and air-fuel-ratio deviations and shortens settling time following step load changes. The results indicate improved disturbance rejection and practical applicability for power-generation engines.</p>

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Model predictive control with adaptive Kalman filter for premixed turbocharged natural gas engine

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月14日

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  • Wenyu Xiong,
  • Qichangyi Gong,
  • Songtao Huang,
  • Jie Ye,
  • Jinbang Xu

摘要

Robust control of natural-gas engines under unknown load disturbances remains challenging due to strong couplings and delays in multi-input multi-output (MIMO) dynamics. This paper presents a control framework that integrates rate-based model predictive control (MPC) with a gain-scheduling scheme driven by an adaptive Kalman filter to enhance performance under unknown load disturbances. A novel adaptation mechanism enables the Kalman filter to rapidly track transient changes in load torque while attenuating steady-state estimation noise. The online torque estimate is used to compute local equilibrium operating points and generate a gain-scheduling parameter matrix that adaptively adjusts MPC behavior to improve transient response. Experimental validation on a laboratory engine demonstrates that the estimator converges quickly during load transients and maintains low steady-state noise; when combined with gain-scheduled MPC, the proposed controller significantly reduces speed and air-fuel-ratio deviations and shortens settling time following step load changes. The results indicate improved disturbance rejection and practical applicability for power-generation engines.