The purpose of this article is to develop optimal control strategies for discrete-time multi-agent systems (DT-MASs) with unknown disturbances, with the goal of enhancing their consensus performance and disturbance rejection capabilities. Complex flight conditions, such as the scenario of multi-unmanned aerial vehicle (multi-UAV) maintaining consensus under strong wind gusts, pose significant challenges for MAS control. To address these challenges, this article develops an optimal controller for UAV-based MASs with unknown disturbances to reach consensus. First, a novel improved nonlinear extended state observer (INESO) is designed to estimate disturbances in real time, accompanied by a corresponding disturbance compensation scheme. Subsequently, the consensus error systems and cost functions are established based on the disturbance-free DT-MASs. Building on this, a policy iterative algorithm based on a momentum-accelerated Actor–Critic network is proposed for the disturbance-free DT-MASs to synthesize an optimal consensus controller, whose integration with the disturbance compensation scheme yields an optimal disturbance rejection controller for the disturbance-affected DT-MASs to achieve consensus control. Comparative quantitative analysis demonstrates significant performance improvements over a standard gradient Actor–Critic network: the proposed approach reduces convergence time by 12.8%, improves steady-state position accuracy by 22.7%, enhances orientation accuracy by 42.1%, and reduces overshoot by 22.7%. Finally, numerical simulations confirm the efficacy and superiority of the method.

This paper presents a leader-follower formation control method for unicycle robots in obstacle-rich environments. A backstepping-based nominal control protocol is firstly proposed to ensure asymptotic formation convergence. Building on it, a safety-guaranteed protocol that integrates distance-based control barrier functions is designed to resolve the relative degree mismatch via a quadratic program(QP). This method achieves formation control while ensuring collision avoidance among all agents as well as between agents and obstacles. Numerical simulations demonstrate the effectiveness of the proposed approach.

This paper investigates bipartite consensus problem of discrete-time multi-agent systems (DTMASs) under impulsive noise. Due to the existence of impulsive noise, it is very difficult for DTMASs to reach bipartite consensus. To address this issue, the maximum correntropy criterion (MCC) algorithm in the control protocol is introduced to weaken or eliminate the impact of impulsive noise on bipartite consensus of DTMASs, and the conditions are derived for DTMASs to reach bipartite consensus by matrix theory and graph theory. Finally, three simulation examples are presented to demonstrate that bipartite consensus of DTMASs subject to impulsive noise can be achieved over the proposed control protocol.

As the informatization, digitization, and intelligence of railways continues to progress, new advantages, such as massive data resources and rich application scenarios, promote the generation of the autonomous intelligent high-speed railway system (AIHSRS). Based on an analysis of the current state of autonomous transportation system research, this article proposes an overall architecture of the AIHSRS, with its connotations and characteristics. Specifically, the fundamental platform of the AIHSRS consists of an entity model layer, data fusion layer, mechanism model layer, and application interface layer. As a result, the AIHSRS supports implementations of autonomous infrastructure, autonomous mobile equipment, autonomous safety and early warnings, autonomous transportation organization, and autonomous transportation service. Finally, the development road map of the AIHSRS is given.

Time-series data is an appealing study topic in data mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods have become mainstream. Echo State Networks (ESN) and Convolutional Neural Networks (CNN) are commonly utilized as deep neural network methods in TSC research. However, ESN and CNN can only extract local dependencies relations of time series, resulting in long-term temporal data dependence needing to be more challenging to capture. As a result, an encoder and decoder architecture named LA-ESN is proposed for TSC tasks. In LA-ESN, the encoder is composed of ESN, which is utilized to obtain the time series matrix representation. Meanwhile, the decoder consists of a one-dimensional CNN (1D CNN), a Long Short-Term Memory network (LSTM) and an Attention Mechanism (AM), which can extract local information and global dependencies from the representation. Finally, many comparative experimental studies were conducted on 128 univariate datasets from different domains, and three evaluation metrics including classification accuracy, mean error and mean rank were exploited to evaluate the performance. In comparison to other approaches, LA-ESN produced good results.

Purpose In recent years, railway systems worldwide have faced challenges such as the modernization of engineering projects, efficient management of intelligent digital railway equipment, rapid growth in passenger and freight transport demands, customized transport services and ubiquitous transport safety. The transformation toward intelligent digital transformation in railways has emerged as an effective response to the formidable challenges confronting the railway industry, thereby becoming an inevitable global trend in railway development. Design/methodology/approach This paper, therefore, conducts a comprehensive analysis of the current state of global railway intelligent digital transformation, focusing on the characteristics and applications of intelligent digital transformation technology. It summarizes and analyzes relevant technologies and applicable scenarios in the realm of railway intelligent digital transformation, theoretically elucidating the development process of global railway intelligent digital transformation and, in practice, providing guidance and empirical examples for railway intelligence and digital transformation. Findings Digital and intelligent technologies follow a wave-like pattern of continuous iterative evolution, progressing from the early stages, to a period of increasing attention and popularity, then to a phase of declining interest, followed by a resurgence and ultimately reaching a mature stage. Originality/value The results offer reference and guidance to fully leverage the opportunities presented by the latest wave of the digitalization revolution, accelerate the overall upgrade of the railway industry and promote global collaborative development in railway intelligent digital transformation.

The effective operation of intelligent dispatching system for high-speed railway (HSR) can reduce the heavy task pressure of dispatchers, thus improving the level of railway transportation organization. Since the railway intelligent dispatching system covers dispatching jobs such as driving, planning, locomotive, freight transport, passenger transport, motor train, construction and maintenance, it is necessary to fully consider their correlation when designing the logical structure of the railway intelligent dispatching system. In this paper, the main basic functions and database of railway intelligent dispatching system are extracted from Data Flow Diagram (DFD), and the logical structure of the system is clustered by using fuzzy Interpretive Structure Model (fuzzy ISM) method. After that, the basic functions are further divided, which can reduce the complexity of system design, the difficulty of system development and the running cost, and improve the system efficiency.

As the current level of higher education in China improves, so too do higher education courses. The key to improving the quality of higher education in China is to improve teaching quality (TQ), while the key to improving the quality of higher education and teaching in China is the key to higher education. It is therefore necessary to formulate and finalize a system of quality assessment of higher education in order to manage higher education. The article aims to analyze the quality of ideological and political (IAP) education in colleges based on deep learning. It analyzes TQ in IOP courses in colleges, the role of quality assessment education, problems in the quality assessment system of teaching, and problems in the design of IAP quality education assessment. Based on the principles to be followed by the referral system, an IAP quality TQ assessment system has been developed and the MATLAB simulation software is based on the teaching network quality evaluation model and test model based on the TQ.

Self-driving vehicles must be equipped with path tracking capability to enable automatic and accurate identification of the reference path. Model Predictive Controller (MPC) is an optimal control method that has received considerable attention for path tracking, attributed to its ability to handle control problems with multiple constraints. However, if the data acquired for determining the reference path is contaminated by non-Gaussian noise and outliers, the tracking performance of MPC would degrades significantly. To this end, Correntropy-based MPC (CMPC) is proposed in this paper to address the issue. Different from the conventional MPC model, the objective of CMPC is constructed using the robust metric Maximum Correntropy Criterion (MCC) to transform the optimization problem of MPC to a non-concave problem with multiple constraints, which is then solved by the Block Coordinate Update (BCU) framework. To find the solution efficiently, the linear inequality constraints of CMPC are relaxed as a penalty term. Furthermore, an iterative algorithm based on Fenchel Conjugate (FC) and the BCU framework is proposed to solve the relaxed optimization problem. It is shown that both objective sequential convergence and iterate sequence convergence are satisfied by the proposed algorithm. Simulation results generated by CarSim show that the proposed CMPC has better performance than conventional MPC in path tracking when noise and outliers exist.

Aiming to address the problem of robots based on QR code navigation being unable to reach QR code nodes in time to correct dead reckoning errors, this paper proposes an improved A* algorithm. The improved method firstly groups all QR code nodes into a dedicated node list, separated from ordinary nodes. It then applies a QR code reward function to weight the nodes in the dedicated node list. Finally, it modifies the weighting of the heuristic function to prioritize generating paths that pass through specific nodes in order to reach the target node. Through simulation experiments, the proposed improved method increases the number of times the robot passes through the QR code nodes while ensuring path safety, effectively reducing dead reckoning errors, and improving the accuracy and efficiency of robot navigation.

The PSO convergence analysis is mainly based on the constant attractor, however, the attractor of PSO algorithm in the evolutionary process is time-varying point. So, the objective of this study is to mainly discuss the random convergence analysis of the standard and improved particle swarm optimization with the time-varying attractor. Its mathematical PSO model with the time-varying attractor is provided to calculate the convergence condition and the corresponding convergence speed. Specifically speaking, spectral radii of the random transfer matrix and the product of two adjacency random transfer matrices are calculated to determine the convergence or the divergence, together with the corresponding convergence speed. Additionally, it also calculates the mean and variance of the first and second order particle swarm optimization system with time-varying attractor. Numerical results highlight that the spectral analysis on some benchmark optimization functions is described to show the effectiveness of the obtained results, while the corresponding analysis is closely related to the objective fitness, the convergence speed, the time-varying attractor, the spectral radius of M(t) and M(t+1)M(t), and swarm convergence behavior in the evolutionary process.

Industrial robot is one kind of complex infrastructure in industrial production and applications. Fault diagnosis is an important part of the intelligent application and monitoring of industrial robots. For multi-axis industrial robot compound fault prediction and diagnosis problem, this paper proposes a fault diagnosis model based on improved multi-label one-dimensional convolutional neural network (ML-SRIPCNN-1D). Firstly, the compound fault data set is enhanced by random sampling and Mixup. Then, the single fault data and compound fault data were trained end-to-end by the improved multi-label one-dimensional convolutional neural network. Finally, accurate diagnosis and prediction of compound faults of industrial robots are implemented. The compound fault data set was derived from a company's multi-axis industrial robot. The characteristic variables of fault diagnosis are torque, current, velocity, position, etc. Compared with SRIPCNN-1D, MLCNN, WT-MLCNN, T-FSM-MLCNN, ELM + AE + SVM, LMD + TDSF + ML-KNN models, the average diagnosis accuracy of ML-SRIPCNN-1D reached 98.67%. The model has good diagnosis effect and high accuracy for the prediction and diagnosis of industrial robot compound fault.

One of the key factors in the safe operation of high-speed railways is the operational status of public works equipment. Constructing the digital model of public works equipment, fusing basic attribute information with detection and monitoring status information, and characterizing the current operating status of public works equipment, will provide more intuitive support for the change law of state change analysis. The correlation analysis between TQI and settlement based on digital model will provide auxiliary decision support for equipment maintenance.

Purpose The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm. Design/methodology/approach Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized. Findings This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm. Practical implications Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast. Originality/value As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

针对目前“高铁+共享汽车”业务场景中存在的旅客汽车使用的数据留痕、汽车参数及变化轨迹等溯源问题,在引入区块链体系中的分布式架构、非对称加密等技术手段的基础上,通过设计具有不可篡改、去中心化等特点的供应链,来解决高铁共享汽车在上架、维修与使用过程中的数据溯源问题,并通过实例应用验证了文中供应链设计的可行性,为进一步提升旅客高铁共享汽车的用户体验提供理论支撑与技术指导.

A novel formation control law in the case of relative damping and nonuniform time-delays is proposed for fractional-order multi-agent systems(FOMASs) in this paper. The nonuniform time-delays can be generally divided into symmetric and asymmetric time-delays. Hence, the formation control algorithm for FOMASs in the case of symmetric time-delays and relative damping is first studied under an undirected network topology. Then, the formation control algorithm for FOMASs in the case of asymmetric time-delays and relative damping is studied under a directed network topology. By the means of frequency-domain theory, algebra graph theory and matrix theory, sufficient conditions are derived to ensure the formation control of FOMASs in the case of nonuniform time-delays and relative damping. Finally, several numerical examples are given and the corresponding simulations are provided to demonstrate the correctness of obtained results.

With the rapid development of China's high-speed railway, the ticket fare structure of high-speed trains is an urgent need of intelligent optimization. A reasonable fare scheme should maximize the revenue of Railway Company, at the same time balancing passengers' choice behavior. This paper applies bi-level programming to deal with this ticket fare optimization problem. A bi-level programming model is built first, of which the upper level programming is the revenue maximization problem, and the lower level programming is the elastic-demand network flow equilibrium problem. Then, a heuristic algorithm based on sensitivity analysis is proposed to solve the model. At last, the actual case and data is applied to verify our method.

Defects in key components of railway vehicles caused by long-running is inevitable. Automated data mining for defect detection is significant for operation and maintenance in railway industry. A huge number of inspection images put pressure on manual analysis. In order to reduce the cost of manual inspection, this paper proposes an automated data mining method for defect detection named multi-channel detection framework (MCDDF). This method regards defect detection task as two stages pipeline, from coarse to fine process. Including one channel for locations and classifications of key components, and the other for judging defects categories of key components. Experiments on high-speed train defect dataset and some frequently used datasets, from various domains, verifies effectiveness of MCDDF over previous approaches.

青藏铁路作为川藏铁路规划与建设的重要参照标准,从空间布局,运输效率和经济社会正外部性等方面对其稳定性进行评价尤为必要.本文根据铁路规划,建设与运营的稳定性内涵来设计指标,利用AHP-FCE方法赋予指标权重,依托三角图论构建模型对青藏铁路进行综合评价,借助Grapher10来描述其演进路径.结果表明,2007—2014年间青藏铁路布局处于基本稳定状态,总体演进路径呈现由强稳定转变为一般稳定,再转变为弱稳定的阶段性特征,其中,2010年青藏铁路所呈现出的稳定性最高.

High-speed Railway has the characteristics of high technical complexity, fast running speed, large passenger capacity, small headway, difficult rescue, and high safety requirements. The occurrence of emergencies will form chain response and amplification effect, which puts forward new and higher requirements for the security early warning and rapid disposal of emergencies. At present, the emergency resources of High-speed Railway are deployed at two levels, China Railway and Railway Bureau, which is not conducive to the unified allocation of resources. This paper integrates cloud computing and big data technology, studies the scheduling problem of High-speed Railway emergency service resources, virtualizes High-speed Railway emergency resources into service pools, establishes a global optimization scheduling model of High-speed Railway emergency resource service pools across headquarters and regions, and proposes a cloud service resource scheduling algorithm based on double-layer particle swarm optimization. The research of this paper has theoretical guiding significance for the construction of High-speed Railway emergency cloud platform.