# Research on Tracking Control of Urban Rail Trains Based on Improved Disturbance Observer
###### Abstract
In [13], an adaptive sliding mode controller was designed to ensure stable tracking of the train under external disturbances and to solve the singularity issue caused by the terminal sliding mode control. The design of the output feedback controller using the linear decoupling method was present in [14] to solve the tracking control under uncertainty and disturbance of train parameters. Although the mentioned above anti-disturbance methods have satisfactory results in terms of system control accuracy, direct disturbance suppression is not considered in the controller design process, and most of them are used to attenuate disturbances by indirect means. In such a case, it is difficult for these controllers to respond quickly when strong disturbances are present. The disturbance observer-based control (DOBC) approach is well recognized for improving the robustness of the system and provides a way to proactively handle unknown disturbances [15]. The DOBC method has been widely used in various fields. In [16], sliding-mode manifolds satisfying finite-time convergence are designed for second-order nonlinear systems, and a nonlinear DOBC strategy is proposed and theoretically verified in the fields of robotic arms and magnetic levitation systems. In [17], for the trajectory tracking problem of a quadrotor system under gusty wind disturbance, a disturbance observer based on LQR (linear-quadratic regulator) and KF (Kalman filter) is proposed, which substantially minimizes the error in position tracking. In [18], the dynamic performance of the permanent magnet synchronous motor drive system is significantly improved by introducing a disturbance observer to eliminate load disturbances. In high-speed train cruise control, the disturbance observer is used to estimate unknown perturbations to improve system robustness, but only if the disturbance model is known [19]. The nonlinear disturbance observer is introduced into the maglev system to ensure the stable suspension of the train, but the train model is based on the single point-mass, and the accuracy needs to be improved [20]. In [21], a sliding mode disturbance observer is proposed for the uncertainty in the braking control of the URTs, which has excellent compensation ability but does not give experimental results at the operation switching points of the train. Based on the above analysis, this paper investigates an improved disturbance observer-based train tracking control method. The method is based on the multiple point-mass model for URTs, and introduces the disturbance observer to estimate the unknown disturbances to the train during operation and then feeds the estimated values back into the system input to achieve the purpose of attenuating the disturbances. The introduction of the disturbance observer not only improves the robustness of the train system but also ensures that the URTs can quickly track the target curves. In addition, rigorous reasoning is given for the stability of the closed-loop system for the URTs. The evaluation of the control accuracy and safety of the system under the two different control methods is given through comparative simulation experiments with the traditional disturbance observer-based control (TDOBC) method. The major contributions are summarized as follows. 1. In this paper, to more accurately describe the motion state of the URTs and improve the control accuracy, consider the coupling force between carriages to establish the multiple point-mass model. An expression for the state space form with partial actuator failure is also given. 2. In order to enhance the anti-disturbance ability of the URTs system, an improved disturbance observer is proposed for unknown external disturbances to estimate and attenuate the unknown disturbances online. The proposed disturbance observer-based controller has high control accuracy and anti-disturbance capability. 3. The proposed disturbance observer in this paper does not require a priori knowledge of unknown disturbances and can estimate attenuation for more types of disturbances.