In the paper, the traction model of the heavy-haul train is formulated firstly in a novel way. And then, a decentralized cooperative controller is proposed to achieve autonomous driving in the traction process of heavy-haul train. The proposed controller includes two parts, one is the coupler compensator, the other is the cooperative controller, and then the power is decentralized on the locomotive. The coupler compensator solves the thorny coupling problem of coupler force by using the fully-actuated and multi-agent ideas. It is converted into the platoon problem in the vehicle. While the cooperative controller addresses the synchronization problem of velocity among cars with the help of some additional sensors and devices. In addition, the stability analysis of the system with saturation actuator is also considered in this paper. And then, a numerical simulation shows the effectiveness of the proposed controller with an actual case in the light of Matlab. Finally, our method’s correctness is verified by Universal Mechanism (UM) platform.

In this paper, the problem of secondary voltage restoration in an islanded microgrid (MG) is considered, in which the actuator of the distributed generators (DGs) may coexist with partial loss of effectiveness (PLOE) fault and bias fault. For each DG, an adaptive event-triggered fault-tolerant (ETFT) control protocol is designed to compensate for the effects of actuator faults in the DGs, thereby restoring the voltage to the reference value. The dependent event triggering mechanism saves the processor’s computational resources. A desirable feature of the protocol proposed in this paper is that the control protocol relies only on relative information between neighboring DG’s, independent of global information about the network graph, fault boundaries, and network scale. It means that the protocol is implemented in a fully distributed framework. The protocol fully applies to the common ETFT consensus control problem of linear multiagent systems (MASs) with actuator faults. Furthermore, comprehensive theoretical arguments for consensus stability and analysis of Zeno behavior ensure the approach’s feasibility. The simulation results verify the effectiveness of the algorithm.Note to Practitioners—This paper aims to propose a fully ETFT control protocol for secondary voltage restoration of an islanded MG. The control protocol consists of a linear term and a nonlinear time that compensates for the multiplicative and additive fault of the actuator. Moreover, DGs’ communication structure and global fault boundaries may be unknown in practice. Hence, adaptive coupling gains that depend only on the sampled relative information of DGs are introduced to estimate the controller gain, thus avoiding global information. A feasible strategy is provided for industrial applications.

This article investigates the energy operation problem based on We-Energy (WE), a novel full-duplex model in Energy Internet (EI). A dual-objective optimal energy operation model of WE is formulated with the consideration of economical benefit and security operation under different time scenarios. Due to the inaccurate model of distributed generation devices and loads, a multipolicy convex hull reinforcement learning (MCRL) algorithm is proposed. It can find the multiobjective strategy set with model-free feature. Moreover, considering the limitations of artificial intelligence technology and the human advantages in information processing for complex task, a two-channel Human-in-the-loop (HITL) method is designed to combine with MCRL to avoid decision-making risks. The one channel of HITL can evaluate the operation strategy by human under normal conditions so that the understanding of human for complex operating conditions can be incorporated into the machine learning algorithms to improve the confidence of intelligent systems. The other channel of HITL can allow human to participate in real-time adjustment under abnormal conditions to avoid system out of control. Simulation studies of modified EI are confirmed that the proposed algorithm can improve system performance effectively.

Intensive use of libraries in Java projects brings potential risk of dependency conflicts, which occur when a project directly or indirectly depends on multiple versions of the same library or class. When this happens, JVM loads one version and shadows the others. Runtime exceptions can occur when methods in the shadowed versions are referenced. Although project management tools such as Maven are able to give warnings of potential dependency conflicts when a project is built, developers often ask for crashing stack traces before examining these warnings. It motivates us to develop Riddle, an automated approach that generates tests and collects crashing stack traces for projects subject to risk of dependency conflicts. Riddle, built on top of Asm and Evosuite, combines condition mutation, search strategies and condition restoration. We applied Riddle on 19 real-world Java projects with duplicate libraries or classes. We reported 20 identified dependency conflicts including their induced crashing stack traces and the details of generated tests. Among them, 15 conflicts were confirmed by developers as real issues, and 10 were readily fixed. The evaluation results demonstrate the effectiveness and usefulness of Riddle.

Intensive dependencies of a Java project on third-party libraries can easily lead to the presence of multiple library or class versions on its classpath. When this happens, JVM will load one version and shadows the others. Dependency conflict (DC) issues occur when the loaded version fails to cover a required feature (e.g., method) referenced by the project, thus causing runtime exceptions. However, the warnings of duplicate classes or libraries detected by existing build tools such as Maven can be benign since not all instances of duplication will induce runtime exceptions, and hence are often ignored by developers. In this paper, we conducted an empirical study on real-world DC issues collected from large open source projects. We studied the manifestation and fixing patterns of DC issues. Based on our findings, we designed Decca, an automated detection tool that assesses DC issues' severity and filters out the benign ones. Our evaluation results on 30 projects show that Decca achieves a precision of 0.923 and recall of 0.766 in detecting high-severity DC issues. Decca also detected new DC issues in these projects. Subsequently, 20 DC bug reports were filed, and 11 of them were confirmed by developers. Issues in 6 reports were fixed with our suggested patches.

This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.

This paper is concerned with the synchronization problems for an array of neural networks with hybrid coupling and interval time-varying delay. First, a novel augmented Lyapunov-Krasovskii functional (LKF) method is proposed to develop delay-dependent synchronization criteria for the networks, which makes use of more relaxed conditions by employing the new type of augmented matrices with Kronecker product operation. The proposed method can handle a multitude of Kronecker product operations in the LKF and alleviates the requirements of the positive definiteness of some conditional matrices which are usually considered in the existing methods for complex networks. This leads to a significant improvement in the performance of the synchronization criteria, i.e., less conservative synchronization results can be obtained. Meanwhile, the case of fast time-varying delay can also be handled by the proposed method. Furthermore, based on the derived criteria, a robust synchronization criterion is obtained for the system with uncertainties both in coefficient and coupling matrix terms. Since an expression based on linear matrix inequality is used, the proposed criteria can be easily checked in practice. Finally, numerical examples are provided to show the effectiveness of the proposed method.

This paper studies the stability problem of a class of recurrent neural networks (RNNs) with multiple delays. By using an augmented matrix-vector transformation for delays and a novel line integral-type Lyapunov-Krasovskii functional, a less conservative delay-dependent global asymptotical stability criterion is first proposed for RNNs with multiple delays. The obtained stability result is easy to check and improve upon the existing ones. Then, two numerical examples are given to verify the effectiveness of the proposed criterion.

In this paper, a weighting-delay-based method is developed for the study of the stability problem of a class of recurrent neural networks (RNNs) with time-varying delay. Different from previous results, the delay interval [0, d(t)] is divided into some variable subintervals by employing weighting delays. Thus, new delay-dependent stability criteria for RNNs with time-varying delay are derived by applying this weighting-delay method, which are less conservative than previous results. The proposed stability criteria depend on the positions of weighting delays in the interval [0, d(t)] , which can be denoted by the weighting-delay parameters. Different weighting-delay parameters lead to different stability margins for a given system. Thus, a solution based on optimization methods is further given to calculate the optimal weighting-delay parameters. Several examples are provided to verify the effectiveness of the proposed criteria.

This paper studies a class of new neural networks referred to as switched neutral-type neural networks (SNTNNs) with time-varying delays, which combines switched systems with a class of neutral-type neural networks. The less conservative robust stability criteria for SNTNNs with time-varying delays are proposed by using a new Lyapunov-Krasovskii functional and a novel series compensation (SC) technique. Based on the new functional, SNTNNs with fast-varying neutral-type delay (the derivative of delay is more than one) is first considered. The benefit brought by employing the SC technique is that some useful negative definite elements can be included in stability criteria, which are generally ignored in the estimation of the upper bound of derivative of Lyapunov-Krasovskii functional in literature. Furthermore, the criteria proposed in this paper are also effective and less conservative in switched recurrent neural networks which can be considered as special cases of SNTNNs. The simulation results based on several numerical examples demonstrate the effectiveness of the proposed criteria.