This paper proposes a disturbance observer-based discrete sliding-mode control scheme with the variable sampling rate control for the marine diesel engine speed control in the presence of system uncertainties and disturbances. Initially, a sliding-mode controller based on the fast power reaching law is employed, which has a good dynamic quality of the arrival stage and can suppress chattering. To satisfy the practical requirements in the digital controller and the crank angle-based fuel injection in engine speed control, the proposed method is discretized under the variable sampling rate condition. A disturbance observer based on the second-order sliding-mode control is designed to compensate the system uncertainties and disturbances, by doing such the requirement of the parameters of the sliding-mode controller to be reduced significantly. In addition, a cylinder-by-cylinder mean value engine model (MVEM) is built by restructuring the combustion torque model, based on which numerical simulations are carried out by comparing the proposed method with PID and the extended state observer (ESO)-based sliding mode controllers. The common operation situations of the marine diesel engines are taken into account, including starting process, acceleration and deceleration, load variation, and varied propulsion system parameters. The results demonstrate that the proposed disturbance observer-based sliding-mode controller has prominent control performance and strong robustness.

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.

Experimental measurement of radiation dose in a dedicatedbreast CT system * SHEN Shan-Wei( ) 1,2,3;1) WANG Yan-Fang( ) 2,3 SHU Hang( ) 2,3 TANG Xiao( ) 2,3 WEI Cun-Feng( ) 2,3 SONG Yu-Shou( ) 1 SHI Rong-Jian( ) 2,3 WEI Long( ) 2,3

As a more efficient power machine, diesel engine is widely used in industry, large vehicles, ships, power generation and other industries. Because of its high thermal efficiency, low fuel consumption, strong power and long service life, diesel engine will continue to occupy a leading position in its application field in the coming decades. Therefore, the speed control of diesel engine has always been a hot research topic. However, there are some disadvantages if only using PID controller for diesel engine speed control. This paper studies the structure of diesel engine speed control and the form of neural network. The combination of BPNN (Back Propagation Neural Network) and PID control makes it adjust the PID parameters in real time according to the speed error and achieve the tracking of actual speed and ideal speed. The realization of this method solves the problem of difficult PID parameters selection to a certain extent. And it has the characteristic of automatically adjusting parameters under variable working conditions. So that the engine can still maintain the desired speed under variable working conditions.