Distributed Cooperative Tracking Control Strategy for Virtual Coupling Trains: An Event-Triggered Model Predictive Control Approach

Abstract

Virtual coupling (VC) technology has received much attention because of its significant advantages in enhancing the railway transport capacity; it achieves efficient train coupling operation through advanced communication technology. However, due to the uncertainty of the operating environment, a stable and effective control system is the key enabler for realization. In this paper, an event-triggered distributed model predictive control (ET-DMPC) method is proposed for the cooperative tracking control of virtual coupling trains (VCTS), considering resource limitations and multiple constraints. Firstly, a distributed model predictive control (DMPC) framework is designed. Based on the established VCTS dynamics model of the dual-leader communication topology, a distributed optimization objective function and safety constraints containing state information of the neighboring train system are constructed. Secondly, due to the limitations of communication and computational resources, the event triggering (ET) mechanism is further introduced, and an ET-DMPC method suitable for VCTS is proposed. The trigger condition of each unit train is designed on the premise of guaranteeing system stability, under which the system can guarantee the input-state stability (ISS), and the recursive feasibility of the system is proven via theoretical analysis. Finally, the VCTS composed of four CRH380A unit trains is used as the control object for simulation experiments, and through two sets of experimental simulation analysis, the effectiveness of the proposed method is verified.

An Improved Adaptive Iterative Extended Kalman Filter Based on Variational Bayesian

“…Given the extensive use of nonlinear systems [1,2] in various domains, including radar [3,4], communication [5,6], navigation [7,8], control [9,10], finance [11], and statistics [12,13], the nonlinear dynamics of these systems require sophisticated filtering techniques for robust and precise estimation [14,15]. However, the effectiveness of these filtering techniques significantly diminishes in the presence of heavy-tailed noise and errors.…”

Fault-tolerant control of bullet train based on fuzzy adaptive control

“…With the continuous optimization of control theory and control methods, research on train operation control mainly focuses on energy conservation and speed position trajectory tracking. Among them, the main control methods include sliding mode control [1], PID control [2], adaptive control [3][4], and predictive control [5]. Li et al [6] applied robust iterative learning and robust adaptive control methods to train tracking control in response to external disturbances and parameter uncertainties.…”