Locomotive Adhesion State Estimation Under Low Adhesive Condition

Tractive force is the ultimate drive for locomotives,which highly depends on track adhesion.The adhesion availability must be further improved to increase tractive force,which is mainly achieved by adhesion control.One difficulty of implementing adhesion control is the estimation of adhesion state,affected by the environment,the track state,and the precision of measurement.The adhesion between the track and the locomotive in operation exhibits highly nonlinear and time-variant characteristics.Therefore,to improve adhesion control performance,the adhesion state must be precisely estimated.It is difficulty to describe the adhesion process with an accurate mathematical model,because it is subject to large uncertainty and many other effects.Consequently,traditional state observers and the standard Kalman filter work poorly in such systems.In this thesis,we present research focusing on state estimation of uncertain systems.The fundamental idea and principle of Kalman filter are first introduced.After that,the cubature Kalman filter(CKF)and the improved strong tracking cubature Kalman filter(ISTCKF)are derived and analyzed.Then,we build the dynamic model for locomotives with one or multiple wheelsets.Combining the locomotive dynamic model and the Kalman filters(CKF or ISTCKF),we can estimate the wheel speed,the locomotive speed as well as the adhesion coefficient.This strategy is simulated on the MATLAB/SIMULINK platform,which is promising for adhesion control in practice.Through studying locomotive speed estimation under low adhesion,we propose a modified strong tracking filter algorithm,whose accuracy is further improved by increasing the filter abnormality threshold and decreasing the possibility of fading factor generation.Additionally,by introducing strong tracking fading factor into the estimation covariance matrix,an improved strong tracking cubature Kalman filter(ISTCKF)method is developed.As aforementioned,adhesion state estimation is critical to the dynamic control of locomotives,due to the fact that its accuracy can influence the control performance to a large extent.In a locomotive system,bogies serve as actuators for fast response,which can fast and accurately adjust the driving force and the braking force,as well as information units,which can report the driving torque and the driving wheel speed in real time.Thus,it is expected to further improve locomotive speed estimation by incorporating the data collected from bogies.For example,distributed locomotive speed estimation based on ISTCKF can control the overall speed by coordinating two bogies.As independent components of a locomotive,the speed of two bogies can be measured separately.Give the speed of two bogies,the comprehensive judgement module can yield the optimal locomotive speed estimation by kinetics calculation.In summary,simulation results show that this state estimation system can significantly promote the accuracy and performance of locomotive speed estimation.