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Aiming at the cooperative control problem of multi-agent system, the fixed-time containment control of general second-order multi-agent system under mismatched disturbances is studied. Firstly, the problem of multi-agent system fixed-time containment control in ideal state is analyzed, and a distributed control protocol based on variable structure control is proposed. Then, a nonlinear fixed-time disturbance observer is designed, aiming at the mismatched disturbance, and a fixed-time control protocol based on the nonlinear disturbance observer is proposed. Finally, the effectiveness of the proposed control protocol under switching topology and multi-source disturbance is verified by numerical simulation.

A multi‐motor coordinated tracking control strategy based on a disturbance sliding‐mode observer and an anti‐saturation non‐singular fast‐terminal sliding mode is proposed to address the issues of slow convergence and controller output saturation in multi‐motor coordinated control systems. Firstly, a mathematical model of a multi‐motor traction system considering uncertain parameter perturbations, external disturbances, and dead zones was established. Secondly, a disturbance sliding‐mode observer was designed based on the mathematical model to eliminate motor disturbances and estimate the torque. The observer's forward compensation was added to design a total‐consensus‐based fast non‐singular terminal sliding‐mode controller. Then, a fast anti‐saturation auxiliary system with fast finite‐time convergence was constructed. Finally, a comparative experiment was conducted with traditional anti‐saturation sliding‐mode control to demonstrate that the proposed method had faster convergence, stronger disturbance rejection, and better tracking performance in the presence of multi‐motor parameter perturbations, unknown disturbances, and input saturation. [ABSTRACT FROM AUTHOR]

Cyclic air braking is a key element for ensuring safe train operation when running on a long and steep downhill railway section. In reality, the cyclic braking performance of a train is affected by its operating environment, speed and air-refilling time. Existing optimization algorithms have the problem of low learning efficiency. To solve this problem, an intelligent control method based on the deep Q-network (DQN) algorithm for heavy-haul trains running on long and steep downhill railway sections is proposed. Firstly, the environment of heavy-haul train operation is designed by considering the line characteristics, speed limits and constraints of the train pipe's air-refilling time. Secondly, the control process of heavy-haul trains running on long and steep downhill sections is described as the reinforcement learning (RL) of a Markov decision process. By designing the critical elements of RL, a cyclic braking strategy for heavy-haul trains is established based on the reinforcement learning algorithm. Thirdly, the deep neural network and Q-learning are combined to design a neural network for approximating the action value function so that the algorithm can achieve the optimal action value function faster. Finally, simulation experiments are conducted on the actual track data pertaining to the Shuozhou–Huanghua line in China to compare the performance of the Q-learning algorithm against the DQN algorithm. Our findings revealed that the DQN-based intelligent control strategy decreased the air braking distance by 2.1% and enhanced the overall average speed by more than 7%. These experiments unequivocally demonstrate the efficacy and superiority of the DQN-based intelligent control strategy. [ABSTRACT FROM AUTHOR]

A bearing fault is one of the major causes of rotating machinery faults. However, in real industrial scenarios, the harsh and complex environment makes it very difficult to collect sufficient fault data. Due to this limitation, most of the current methods cannot accurately identify the fault type in cases with limited data, so timely maintenance cannot be conducted. In order to solve this problem, a bearing fault diagnosis method based on the fractional order Siamese deep residual shrinkage network (FO-SDRSN) is proposed in this paper. After data collection, all kinds of vibration data are first converted into two-dimensional time series feature maps, and these feature maps are divided into the same or different types of fault sample pairs. Then, a Siamese network based on the deep residual shrinkage network (DRSN) is used to extract the features of the fault sample pairs, and the fault type is determined according to the features. After that, the contrastive loss function and diagnostic loss function of the sample pairs are combined, and the network parameters are continuously optimized using the fractional order momentum gradient descent method to reduce the loss function. This improves the accuracy of fault diagnosis with a small sample training dataset. Finally, four small sample datasets are used to verify the effectiveness of the proposed method. The results show that the FO-SDRSN method is superior to other advanced methods in terms of training accuracy and stability under small sample conditions. [ABSTRACT FROM AUTHOR]

The Yolov4 detection algorithm does not sufficiently extract local semantic and location information. This study aims to solve this problem by proposing a Yolov4-based multiscale feature fusion detection system for high-speed train wheel tread defects. First, multiscale feature maps are obtained from a feature extraction backbone network. The proposed multiscale feature fusion network then fuses the underlying features of the original three scales. These fused features contain more defect semantic information and location details. Based on the fused features, a path aggregation network is used to fuse feature maps at different resolutions, with an improved loss function that speeds up the convergence of the network. Experimental results show that the proposed method is effective at detecting defects in the wheel treads of high-speed trains., Author(s): Changfan Zhang [1]; Xinliang Hu [2]; Jing He (corresponding author) [1]; Na Hou [1] 1. Introduction High-speed train fault diagnosis is crucial for the safe operation and maintenance of [...]

In the coordinated control of multiple motors for heavy-haul locomotives, the input value for a motor often exceeds its maximum allowable input value, resulting in the saturation problem. A traction total-amount coordinated tracking control (TACTC) strategy is proposed to address the input saturation of heavy-haul locomotives driven by multiple motors. This strategy reduces control input and suppresses input saturation. First, a multimotor traction model with uncertain parameter perturbations and external disturbances was established. Next, a sliding-mode disturbance observer (SMDO) was designed to reduce the sliding-mode switching gain, thereby decreasing the control input. An auxiliary anti-windup (AW) system was used to weaken the effect of input saturation on tracking performance. Then, the observed value and auxiliary state were fed back to the sliding-mode controller to design a TACTC protocol and ensure that the total amount of traction torque follows the desired traction characteristic curve. Finally, the Matlab/Simulink simulation and RT-Lab semiphysical experiment results show that the proposed strategy can effectively suppress the input saturation problem of multimotor coordinated control., Author(s): Changfan Zhang [1]; Qian Zhang [1]; Jing He [1]; Jianhua Liu (corresponding author) [1]; Xingxing Yang [1]; Songan Mao [2] 1. Introduction Multiple motors together providing traction torque is [...]

This paper investigates a virtual line shafting-based total-amount coordinated control method of multi-motor traction power to solve the traffic safety problem caused by train traction power loss. This method considers the total amount instead of the synchronous control amongst single motors in a multi-motor control system. Firstly, a block diagram of the proposed method is built. Secondly, on the basis of this diagram, an accurate system model with parameter perturbations is constructed. Thirdly, a virtual controller is designed to quickly adjust the output torque of the virtual motor and to realise a tracking control of the reference torque. A totalamount coordinated control strategy based on the integral sliding mode is also designed to keep the total traction power of the multi-motor system constant under uncertain and unknown disturbances. Lyapunov stability theory is used to prove the system stability. The simulation and experiment results verify the effectiveness of the virtual controller and the total- amount coordinated control strategy in guaranteeing system robustness under disturbances and parameter perturbations., 1. Introduction Train safety is an important issue in transportation systems [1]. One key feature of train control technologies is traction system control [2-5]. Previous studies have transformed the traction [...]

The traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. However, it is difficult to obtain these states due to complex adhesion mechanism and changeable operation environment. According to the influence of wheel/rail adhesion utilization on locomotive control action, the wheel/rail adhesion states are divided into four types, namely normal adhesion, fault indication, minor fault, and serious fault in this work. A wheel/rail adhesion state identification method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identification model is constructed using KELM, and then the regularization coefficient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. Finally, based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the effectiveness and feasibility of the proposed method., 1. Introduction The traction performance of heavy-haul locomotive depends on the utilization efficiency of wheel/rail adhesion state [1-4], and the adhesion force required to operate locomotive is achieved by adjusting [...]