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

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.