Research On Speed Control Strategy Of High Pressure Common Rail Diesel Engine Based On Adaptive Algorithm

High-pressure common rail diesel engine is a controlled object with many nonlinear characteristics.It is difficult to obtain good speed control effect in the full range of working conditions using traditional PID algorithm.The current solution is mostly to use segmented PID algorithm to control its speed.Adaptive control is a type of nonlinear control method that deals with uncertain systems.The controller design parameters,such as control gain,can be adjusted online based on the input received by the controlled object to adapt to the system uncertainty.Therefore,this article explores the speed control of high-pressure common rail diesel engine based on the minimum control synthesis algorithm(MCS)for this problem,and verifies the algorithm by borrowing some ideas from model-based design(MBD)and V-mode development.The realization process of the algorithm starts from theory first.Since the minimum control synthesis algorithm is an extension of the model reference adaptive algorithm,it is necessary to obtain a reference model.The reference model is the power system part of the high-pressure common rail diesel engine,which is then simplified into the state space equation.The model parameters are obtained through the Simulink Design Optimization toolbox,and the parameters are identified through the existing data.After obtaining the reference model,it was used as the controlled object to complete the system stability proof based on Popov’s superstability theory.After the system stability is proved by theory,the algorithm is verified by offline simulation.To this end,a zero-dimensional model of a high-pressure common rail diesel engine was built,including a common rail fuel system model,intake and exhaust system,cylinder and in-cylinder combustion model,and crank connecting rod dynamics model.The accuracy check of the model is completed by comparing with the experimental data of 25 different working conditions,the error is less than 10%,and it can be used as the controlled object for offline verification.Offline verification is completed by designing three sets of comparative experiments.The results show that when only the same adaptive coefficient is given,the partial control index of speed response under MCS algorithm control is better than PID algorithm.After that,in order to make the control process closer to reality,the algorithm was verified by semi-physical simulation.Using the Rapid Control Prototyping system of d SPACE as the controller,the ETAS platform was used as a real-time simulator for semi-physical simulation.In the semi-physical simulation environment,the overshoot of300N·m load sudden increase and unloading was reduced by 1.75% and 1.58;the overshoot of 600 N·m load sudden increase and unloading was reduced by 2.91% and 2.41%;900 N·m load suddenly increased and unloaded The amount of overshoot was reduced by 6.42% and5.42% respectively.And by designing experiments with different rail pressures and time-varying parameters of the controlled object,it reflects the good adaptive characteristics of the algorithm.