Research On Optimal Control Of Agricultural Diesel Engines Based On Load Torque Active Observation

The scarification and cultivation of agricultural tractor is one of the most energy consuming agricultural operations.The torque demand for the farming process changes rapidly.However,the turbocharging system is unable to provide enough air in time due to its own response delay,which would lead to decrease in the operation stability and increase of fuel consumption during the load change.In order to improve the transient performance of agricultural machinery,this paper proposes a control strategy based on the combination of active disturbance rejection control,operation load prediction and model parameter self-learning,aiming at solving the control problem in the transient process of agricultural machinery.Firstly,the simulation platform of a diesel engine(6.7L)with variable geometry turbocharger was established,and the accuracy of the model was verified by the experimental data.The response delay of turbocharging system and its influence on air-fuel ratio,engine speed,and fuel consumption rate in the transient process were analyzed using both the diesel engine test bench and the simulation platform.Secondly,in order to solve the control problem of transient operation process,a control algorithm for the transient process of agricultural diesel engine was developed based on the active observation of load torque.The control architecture was divided into three parts: fuel injection control,intake pressure control and parameter self-learning.The load torque model of subsoil shovel was established,and the active disturbance rejection speed control algorithm of transient process was formed by combining this model.The nonlinear tracking differentiator was used to achieve accurate tracking of the changing rate of the working depth with sensor noise.Based on this,the algorithm of agricultural machinery working pattern recognition was developed,and the working depth data of each cycle was collected.The Bayesian linear regression algorithm was used to predict the working depth of next working cycle,which was used to control the air system in advance.The control trajectories of different target boost pressures were compared,and the effects of control advance on air-fuel ratio and pumping loss were systematically studied.The optimal control trajectories under different working depth changing rates were obtained.In order to solve the problem of the variation of soil cultivation resistance after the change of operation place,a parameter learning algorithm of load torque model was designed by using recursive optimization algorithm to ensure the stability and efficiency of the work under variable conditions.Finally,the algorithm was verified by simulation on the high-precision MATLAB/Simulink platform calibrated by experiments.Compared with the traditional control method of agricultural diesel engine,the air supply speed in the transient process was increased by 53.1%,the indicated torque response was accelerated by 44.7%,the engine speed fluctuation was reduced by 94.4%,and the effective fuel consumption rate was reduced by 7.7%.The control scheme proposed in this paper could adapt to different operating habits as well as soil environment and was able to effectively reduce the engine speed fluctuation and effective fuel consumption rate under different operating depth changing rates and operations environments.