Research On Adhesion Control Method For Electric Locomotive

With the rapid development of China’s rail transportation industry,electric locomotives play a very important role in the railway transportation industry.At present,heavy-load railway transportation is the backbone of China’s freight industry and promotes the rapid development of the national economy.Electric locomotive is the sole driving force for heavyload freight trains.The performance of electric locomotive is related to the efficiency of heavyload railway transportation.The locomotive power comes from the traction drive system.The performance of the traction drive control system determines the effective use of traction.Adhesion control plays a key role in the traction drive control system.During the operation of electric locomotives,due to the rolling contact between the wheels and the rails,the wheel-rail contact in the slipping area caused the wheel to idle or slip in poor sliding conditions or when the axle weight was transferred.In the worst case,a derailment accident occurred.The role of adhesion control is to constrain the contact state of the wheel and rail to the adhesion area,and to prevent the wheel from idling or slipping.This paper introduces model predictive control to the adhesion controller design to predict the optimal adhesion point in the current running state in real time to ensure the wheel and rail function in the best adhesion state.First,based on the relationship between the creep ratio of the wheel and rail and the adhesion coefficient,an optimal adhesion point estimation strategy was designed.The best creep rate in the current operating state is searched in real time,and the adhesion coefficient in the current operating state is estimated in real time by an observer.Secondly,an adhesion controller based on model predictive control is designed.The optimal creep rate is used as the reference input of the model predictive controller.A series of cost functions are added to optimize and predict the optimal traction torque in the current operating state.Ensure the wheel-rail contact is in the best adhesion state.A MATLAB /Simulink simulation software is used to establish a single-axis locomotive dynamics model to simulate different rail surface switching states and verify the performance of the MPC adhesion controller.The simulation results show that the MPC adhesion controller can predict the optimal adhesion point and optimize the adhesion control in real time.Then,considering the dynamic coordination between the four axes of the electric locomotive,an optimized adhesion control method based on distributed model predictive control is designed.Each axis of the electric locomotive corresponds to an MPC adhesion controller,and the optimal creep rate is used as the reference input of the MPC adhesion controller.Each MPC adhesion controller maintains coordinated communication and optimizes and predicts the traction torque of the current running state in real time.Moment to ensure that each wheel pair works in the best adhesion state and achieves maximum adhesion utilization.The MATLAB/Simulink simulation software is used to build a four-axle locomotive dynamics model,simulate different rail surface switching states,consider the axle weight transfer situation,and verify the performance of the distributed adhesion controller.Simulation results show that the method can dynamically predict the maximum adhesion point Coordinate and optimize the adhesion level of each wheel.Finally,considering the problem of adhesion control in low adhesion state,an intelligent sanding control strategy based on fuzzy system is designed.By setting the adhesion characteristic curve after sand spreading and simulating the adhesion control during sand spreading,and using actual locomotive running data to test the performance of the fuzzy sanding controller and comparing it with the AHP sanding control,the effective and practical performance of the fuzzy sanding control strategy is verified.