The following operation of multiple high-speed trains (HSTs) with high speed and great density based on the moving block and virtual coupling control system can significantly improve the performance of railway systems concerning operation safety and line capacity. However, it is difficult for HST’s conventional control approach to satisfy the service demand, including safety, energy-saving, and ride comfort, due to the significantly increasing longitudinal interaction between adjacent trains in the following operating conditions. In this paper, the characteristic model of multiple HSTs’ following operation was formulated to effectively and accurately describe train control constraints derived from the coupling relationship among adjacent trains’ operation states. Then, the cooperative model predictive control (CMPC) strategy with a multiobjective rolling optimization scheme is developed. The overall line capacity, energy consumption, and ride comfort of train platoons in the railway section are defined as optimization indexes. Furthermore, some sufficient conditions for the consensus stability of the CMPC of HST are given, based on the proposed multiple HSTs following characteristic model and cooperative optimal control strategy. Simulation experiments are carried out by using HST’s operation data collected from real-world runs. Results demonstrate the effectiveness and feasibility of the proposed modeling and control method.

Obstacle avoidance is an important issue when a wheeled mobile redundant robot manipulator (WMRRM) completes given tasks in complex environment. In this article, a novel vector-based constrained obstacle avoidance (VOA) scheme is designed and analyzed for motion planning of the WMRRM. Specifically, the VOA scheme is first described as a quadratic programming (QP) problem subject to an equality constraint, an inequality constraint, and a bound constraint. Compared with the traditional obstacle avoidance scheme, the proposed VOA scheme can not only achieve subtasks, such as obstacle avoidance and physical limit avoidance when the robot completes the main end-effector task, but also greatly expands the range of feasible spaces. In addition, the VOA scheme is transformed into piecewise linear projection equations (PLPEs) and solved by a linear-variational-inequality-based primal-dual neural network (LVI-PDNN), which can efficiently obtain the optimal solutions to the VOA scheme. Computer simulations demonstrate the reliability, accuracy, and effectiveness of the proposed VOA scheme.

A high-speed electric multiple unit (HSEMU) is a power-distributed train consisting of multiple trailers and motor cars, which is becoming a very handy and powerful transportation tool for our daily life. It is critical to control HSEMU synchronously for improving system performances, but challenging to be achieved due to environmental uncertainties, coupling, and nonlinearity. This paper aims to develop advanced modeling and control techniques to achieve the synchronous tracking control of each car body with harmonic motion. To characterize the dynamics of HSEMU, a multi-adaptive neurofuzzy inference systems (ANFIS) model is built through learning sample data collected from real runs. Then, a model predictive controller is designed to implement the synchronous tracking control of HSEMU on the basis of the multi-ANFIS model. A stability analysis of the closed-loop control system is given. Simulation experiments with comparisons on real HSEMU running data are carried out. Results demonstrate that our proposed modeling and control techniques significantly improve the operational performance of HSEMU in terms of safety, punctuality, comfort, and parking accuracy.

The clustering method has been widely used in data mining, pattern recognition, and image identification. Fuzzy c-means (FCM) is a soft clustering method that introduces the concept of membership. In this method, the fuzzy membership matrix is obtained by calculating the distance between data points in the original space. However, these methods may yield suboptimal results owing to the influence of redundant features. Moreover, FCM is always sensitive to noise points and heavily subject to outliers. In this paper, we propose a method called sparsity FCM clustering with principal component analysis embedding (P_SFCM). We simultaneously conduct principal component analysis (PCA) and membership learning, and then add an additional weighting factor for each data point. The goal of this operation is to identify the noise or outliers. Overall, the benefit of our framework is that it retains most of the information in the subspace while improving the robustness of the noise. In this paper, we employ an iterative optimization algorithm to efficiently solve our model. To verify the reliability of the proposed method, we conduct a convergence analysis, noise robustness analysis, and multi-cluster experiments. Furthermore, comparative experiments are conducted on both synthetic and real benchmark datasets. The experimental results show that the P_SFCM is competitive with comparable methods.

Efficient and safe following operation of a high-speed train (HST) under the moving block system (MBS) has been the trend for high-speed railway (HSR) transportation. However, due to the complex operating environment and changeable operation state of HST during the following operation process, it becomes inapplicable for conventional train control approaches to satisfy HST's multi-objective operation demand including safety, punctuality, energy efficiency, and ride comfort. To resolve this problem, in this paper, practical models for the real-world characteristics of HSR's line parameters and HST's following control, as well as the multi-objective optimization model with some novel metrics, are established for achieving the optimal following control of HST under MBS. Besides, the multi-objective particle swarm optimization algorithm is modified with the preference information of control sensitivity and energy efficiency, so as to efficiently obtain the optimal following control strategy based on the real-time data of HST like velocity, location, and speed restriction. Furthermore, the convergence performance of our proposed method is demonstrated by comparative tests which also help select the proper algorithm parameters. Finally, the efficiency and feasibility of the proposed framework are illustrated by showing some experimental results.

To solve a general time-varying Sylvester equation, a novel integral recurrent neural network (IRNN) is designed and analyzed. This kind of recurrent neural networks is based on an error-integral design equation and does not need training in advance. The IRNN can achieve global convergence performance and strong robustness if odd-monotonically increasing activation functions [i.e., the linear, bipolar-sigmoid, power, or sigmoid-power activation functions (SP-AFs)] are applied. Specifically, if linear or bipolar-sigmoid activation functions are applied, the IRNN possess exponential convergence performance. The IRNN has finite-time convergence property by using power activation function. To obtain faster convergence performance and finite-time convergence property, an SP-AF is designed. Furthermore, by using the discretization method, the discrete IRNN model and its convergence analysis are also presented. Practical application to robot manipulator and computer simulation results with using different activation functions and design parameters have verified the effectiveness, stability, and reliability of the proposed IRNN.