Tracking Control Method For High-speed Trains With States Constraints

With the outstanding features such as energy-efficiency,high operation volume and high transportation efficiency,high-speed railway system has developed rapidly in recent years and has become an important part of comprehensive transport system.Currently,high-speed railway is equipped with train control system based on manual driving mode.With the increasing capacity demand,running density and operation speed,it is the industry planning for high-speed trains to upgrade from manual driving mode to automatic operation mode,which is also an important topic of research for experts and scholars to carry out.The advanced train control method is an important way to realize the safe and efficient operation.However,due to multi-dimensional and multi-state restrictions such as complex line conditions,railway network conditions and temporary speed restrictions,trains are subject to a variety of constraints during operation.Meanwhile,the strong nonlinear operation conditions and unknown external environment greatly affect the accuracy of train tracking control,and also put forward higher requirements for the automatic train control methods.This thesis focuses on the states constraints in the process of train operation in two scenarios: speed tracking control and multi-train tracking control.The main contents are listed as follows:Firstly,the backstepping speed tracking control method based on nonlinear mapping for high-speed trains with the states constraints of position and speed is researched.The dynamics model with states constraints is established to deal with constraints of position and speed during train operation.Then,a kind of nonlinear mapping method is designed to remove the constraints.For the transformed nonlinear system,the error transformation is carried out.The low complexity controller is designed and the speed tracking performance is guaranteed.The effectiveness of states constraints for position and speed is verified by simulation cases.Secondly,the adaptive speed tracking control method to deal with constraints of traction and braking for high-speed trains with input saturation is researched.The dynamic model with states constraints and input saturation is established to deal with the physical constraints of traction and braking.Then,the states constraints are converted to constraints of tracking errors and the errors constraints are realized based on barrier Lyapunov function.Meanwhile,the radial basis function neural network is designed to estimate the unknown resistance.The stable tracking performance of the algorithm is verified by simulation cases considering input saturation and states constraints.Finally,the multi-train tracking control method to deal with the constraint of tracking interval is researched in two scenarios where the initial states are near the equilibrium points and the initial errors are excessive.The dynamic model with tracking interval constraint is established to avoid collision.Based on the restriction of barrier Lyapunov function,distributed controllers with small initial errors and excessive initial errors are designed.The tracking control effects under different scenarios and different control parameters are verified by building the simulation module of train speed control methods.The control methods can realize the stable control of multiple trains with high speed and multiple scenarios.There are 55 figures,8 tables and 69 references in this thesis.