Iterative Learning Control And Its Applications In Operation Control Of High-speed Trains

In the practical engineering applications,there are many control tasks that are performed periodically and repeatedly,which require the output of the controlled system could accurately track the reference trajectory with the finite time interval.Since the existing traditional control methods,such as adaptive control,robust control,etc.,can only guarantee the convergence of tracking error for the infinite time,the aforementioned scenario cannot be satisfied.Nevertheless,with the help of the repetitive operation pattern of the controlled system,the iterative learning control can utilize the previous trial information to enhance the control performance of the current trail,so it has obvious advantages and tremendous potentials in dealing with this type of periodic control tasks.As an important part of promoting the development of the national economy,the operating speed of high-speed trains is more than 200 km/h.Under such the conditions of high speed,the automatic train operation system is proposed to complete the starting,traction,cruising and braking operations of train.The key of implementing the automatic train operation is to ensure the tracking control of each train.Normally,the high-speed train runs periodically on the same railway with the given displacement and velocity,so the iterative learning control is expected to become one of the most ideal control methods of the train control systems.This dissertation investigates the iterative learning control theory and its application in high-speed trains.The main research contents and innovations are listed as follows:1)The adaptive iterative learning tracking control problem for a class of nonlinearly parameterized strict-feedback systems with unknown state delays is investigated,where the backstepping technique is used to devise the tracking controller.By selecting the appropriate Lyapunov-Krasovskii functions to compensate the negative effect of unknown state delays on the stability of control system,an adaptive iterative learning control scheme is proposed to track the given reference trajectory.For the mean time,the hyperbolic tangent functions are used to avoid the controller singularity problem,and the command filter is employed to deal with the 'explosion of complexity' issue common in the control procedure.Specifically,the proposed method is utilized to devise the iterative learning tracking controller of high-speed trains with speed delay.As the main innovation of this research content,it is the first time that the iterative learning control scheme of nonlinearly parameterized strict-feedback systems with unknown state delays is investigated.2)The tracking control problem of high-speed trains with input constraints is investigated,where the influence caused by the distribution and output capacity of power systems on the control systems is fully considered.By virtue of the multi-particle model of trains,an adaptive iterative learning control scheme is proposed to drive the high-speed train to track the given reference displacement and velocity,in which the estimations of unknown time-varying parameters are constantly adjusted via the iterative learning during the control process.Following the design procedure of backstepping technique,an input-dependent auxiliary system is introduced to compensate the influence of input constraints.As the main innovation of this research content,it is the first time to study the devise of adaptive iterative learning tracking controller of high-speed with input constraints by means of the multi-particle model.3)The autonomous cooperative tracking control of high-speed train is investigated,in which each car of train is considered as a self-propelled individual,the entire train is described as a multi-agent system with the form of longitudinal formation,and the distributed collaborative tracking control problem of the high-speed train is formalized as the consensus tracking problem of a class of multi-agent systems.By virtue of the controller design method of multiagent systems,a distributed adaptive iterative learning control scheme of high-speed train is proposed to realize the accurate tracking of reference displacement and velocity,while maintaining the consistency of the operational states of each car.As the main innovation of this research content,it is the first time to integrate the distributed cooperative operation of the single train with the periodic repetitive operation pattern to study the devise of distributed adaptive iterative learning tracking controller for high-speed train.The research work in this dissertation is not only the development and improvement of the theoretical system of iterative learning control,but also is expected to solve the practical engineering problems,so it has important theoretical significance and practical value.