High Speed Train Operation Control Algorithm Based On Extended State Observer

In recent years, safety, efficiency, comfort and environmental protection have become the development direction of the high speed railway, and the requirements for the adaptability to the environment are more and more critical. For the feature of the high speed railway, such as the high running speed, the long mileage and the complex external environment, the abilities of the speed curve tracking and the adhesive anti-slip in the train operation control system will be directly related to the performance of the safety and efficiency. In the favorable operation environment, the train pursues tracking the target speed curve precisely; while in adverse running conditions, the anti-slip control will guarantee the safety of the train. Therefore, how to realize the secure and the stable operation of the train under different external environment has become an important research topic in the field of the high speed train. The traction control problem in the process of the train operation and the maximum adhesion control problem based on the extended state observer is discussed as follows:Firstly, the accurate train trajectory tracking control algorithm based on the extended state observer is studied. When operating in good running condition, it aim at tracking target speed curve precisely. The single-point-mass model is structured. The novel control algorithm is based on the extended state observer which estimates the basic train resistance, the gradient resistance and the curve resistance as "total resistance". Using the genetic algorithm to optimize parameters of the observer. The simulation results show that the designed control algorithm based on the extended state observer has better tracking performance and the high anti-interference performance in the good line condition and time-varying environment.Secondly, the sliding mode control algorithm based on the extended state observer under wheel-slip is designed. When the operating conditions of the section of the rail surface changing, the goal is the precise tracking to the target speed curve joining the slip rate as constraints. The single-point-mass dynamical model based on the slip ratio and the adhesion coefficient is established. The new control algorithm is designed based on the improved the extended state observer combining with the sliding mode control. The simulation results show that the designed algorithm can track the target speed curve, and keep the slip rate in allowable range under the good line condition, the time-varying environment and the metabolic rail surface conditions.Finally, the biggest adhesion control problem based on the extended state observer is researched. When the train running in the extreme environment, such as wind, rain and snow weather conditions, the goal of control is the biggest adhesion. According to the relationship between the adhesion coefficient and the slip rate, a super-twisting-like control algorithm based on extended state observer is designed. Prove that the stability and the convergence boundary of the designed algorithm. The optimal slip ratio is derived based on the least-square method by using the high order extended state observer to estimate model parameters of high order differentials. Simulation result verifies that the proposed control strategy can steer the velocity of train to obtain the maximum adhesion force.