In this paper, in order to handle the high nonlinearity and the sophisticated disturbance in marine engines, a variable sampling rate based active disturbance rejection controller is developed for engine speed control. In the proposed method, the Active Disturbance Rejection Control (ADRC) is designed with the consideration of the practical application in engine speed control that is known as the Crank-angle (CA) based or event-based sampling and control, which means the sampling interval varies with the engine speed. Such a problem has not been discussed in any previous study regarding the application of ADRC in engine control. To this end, this paper discusses the convergence of the variable sampling rate based Extended State Observer (ESO), as well as its parameters that guarantee stability. To verify the proposed control scheme more properly, a cycle-detailed hybrid nonlinear engine model is employed. Finally, simulations are carried out on the Hardware-in-the-loop (HIL) system to assess the superiority of the proposed strategy. The comparative results with a Fuzzy-Proportional-Integral-Derivative (PID) controller demonstrate that the proposed control scheme has better adaptation to engine speed, load disturbances, and stronger robustness towards model uncertainties, which indicates a promising reduction of time and burden for calibrating the controller. It also proved that the proposed CA based ADRC by variable sampling rate method outperforms the general fixed sampling rate ADRC, which is widely used in previous works. Moreover, the successful application of the proposed algorithm via CA based strategy in a real Engine Control Unit (ECU) indicates its huge potential in practical engine control.

The application of model predictive control (MPC) algorithm in the fixed phase control of marine diesel engine speed is studied under the premise of considering model mismatch and external disturbance. Firstly, the steady-state error problem of conventional MPC controller is solved by changing nonlinear model to incremental form. Furthermore, discrete disturbance observer (DO) is introduced in the feedback correction, which can filter out the high-frequency disturbance and reduce the requirement of algorithm on the accuracy of model. Then, considering that nonlinear MPC based on DO (DONMPC) requires a large amount of online computation, the algorithm is simplified by preliminarily converting the nonlinear model to linear model. Through analysis, the controller performance of the two models is similar. Furthermore, considering that the speed of marine diesel engine is usually set to a few fixed reference values, a linear multi-model predictive controller based on DO (DOLMMPC) with less online calculation is proposed. Finally, the designed controllers are verified by experiments. The software simulations of the designed controllers and the PID controller are carried out on the cylinder-by-cylinder mean value engine model (MVEM). It is proved that the algorithm simplification method retains the control performance of the DONMPC algorithm, and the control performance of the designed two controllers is better than the PID controller. Moreover, the DOLMMPC controller and PID controller are tested on the semi-physical simulation platform. The results demonstrate that the DOLMMPC controller can meet the computational power limit of the microprocessor in practical engineering.