制动系统CAN网络测试研究

CAN(Controller Area Network)总线以其高时效性、高可靠性等优点,占据目前列车制动系统制动网络主导地位,因而为了提高制动系统网络整体性能,制动系统CAN网络测试技术应运而生。本文基于现有列车制动CAN网络,介绍了网络测试验证内容,测试工具与技术并实现了网络自动化测试。

探析日本三菱公司的制动系统新技术

对车辆生产单位而言,其中最基本的要求就是保障车辆运输的安全稳定,在尽量实现小型化的基础上,降低成本,并满足国家的环境保护要求。而日本三菱公司的制动系统,在一定程度上代表了目前世界上最高的制动系统水平,具有先进性。文章详细介绍了日本三菱公司制动系统的新技术,并对技术的特征、应用等作出详细分析。

Adaptive and distance-driven power control scheme in mobile ad hoc networks

In order to save the energy and reduce the latency of the end-to-end transmission in mobile ad hoc networks an adaptive and distance-driven power control ADPC scheme is proposed by means of distance research in random geometrics. Through mathematical proof the optimal number of relay nodes and the optimal location of each node for data transmission can be obtained when a distance is given.In the ADPC first the source node computes the optimal number and the sites of the relay nodes between the source and the destination nodes.Then it searches feasible relay nodes around the optimal virtual relay-sites and selects one link with the minimal total transmission energy consumption for data transmission.Simulation results show that the ADPC can reduce both the energy dissipation and the end-to-end latency of the transmission.

NEURAL NETWORK REALIZATION OF STRUCTURAL VIBRATION CONTROL USING MR DAMPER

Magnetorheological (MR) dampers are one of the most promising new devices for civil infrastructural vibration control applications. However, due to their highly nonlinear dynamic behavior, it is very difficult to obtain of a mathematical model of inverse MR damper that has an explicit relationship between the desired damper force and the command signal (voltage). This force voltage relationship is especially required for the structural vibration control design and simulation using MR dampers. This paper focuses on using a neural network (NN) technique to emulate the inverse MR damper model. The output of the neural network can be used to command the MR damper for generating desired forces. Numerical simulations are also presented to illustrate the effectiveness of this inverse model in semi active vibration control using MR dampers.

Necessary and Sufficient Conditions for Consensus in Third Order Multi-Agent Systems

We deal with a consensus control problem for a group of third order agents which are networked by digraphs.Assuming that the control input of each agent is constructed based on weighted difference between its states and those of its neighbor agents, we aim to propose an algorithm on computing the weighting coefficients in the control input. The problem is reduced to designing Hurwitz polynomials with real or complex coefficients. We show that by using Hurwitz polynomials with complex coefficients, a necessary and sufficient condition can be obtained for designing the consensus algorithm. Since the condition is both necessary and sufficient, we provide a kind of parametrization for all the weighting coefficients achieving consensus. Moreover, the condition is a natural extension to second order consensus, and is reasonable and practical due to its comparatively decreased computation burden. The result is also extended to the case where communication delay exists in the control input.

Digital Coal Mine Integrated Automation System Based on ControlNet

A three-layer model for digital communication in a mine is proposed. Two basic platforms are discussed: A uniform transmission network and a uniform data warehouse. An actual,ControlNet based,transmission network plat-form suitable for the Jining No.3 coal mine is presented. This network is an information superhighway intended to inte-grate all existing and new automation subsystems. Its standard interface can be used with future subsystems. The net-work,data structure and management decision-making all employ this uniform hardware and software. This effectively avoids the problems of system and information islands seen in traditional mine-automation systems. The construction of the network provides a stable foundation for digital communication in the Jining No.3 coal mine.

A TCP Performance Enhancement Scheme in Infrastructure based Vehicular Networks

Transmission Control Protocol （TCP） in infrastructure based vehicular net- works is dedicated to support reliable Intemet services for mobile users. However, an end-to- end TCP flow not only experiences some com- mon challenges in wireless mobile networks, such as high packet loss rate, medium access competition, unstable wireless bandwidth, and dynamic topology, etc., but also suffers from performance degradation due to traffic congestion at the Road-Side Units （RSUs） that connect the wireline and wireless networks. In order to address the challenging issues related to reliable TCP transmissions in infrastruc- ture based vehicular networks, we propose an RSU based TCP （R-TCP） scheme. For wireline source nodes, R-TCP adopts a novel flow control mechanism to adjust transmission rates according to the status of bottleneck link. Specifically, during the short wireless connec- tion time in Infrastructure based vehicular net- works, R-TCP quickly chooses an ideal trans- mission rate for data transmissions instead of activating the slow start algorithm after the connection is established, and successfully avoids the oscillation of the transmission rate. Simulation results show that R-TCP achieves great advantages than some relate proposals in terms of throughput, end-to-end delay, and packet loss rate.

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

Abstract--In this paper, an adaptive neural network （NN） control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions （BLFs） with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks （NNs）, non- linear pure-feedback systems, time-varying constraints.

Fuzzy neural network control of underwater vehicles based on desired state programming

Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn’t been solved very well yet. A novel method of control based on desired state programming was presented, which used the technique of fuzzy neural network. The structure of fuzzy neural network was constructed according to the moving characters and the back propagation algorithm was deduced. Simulation experiments were conducted on general detection remotely operated vehicle. The results show that there is a great improvement in response and precision over traditional control, and good robustness to the model’s uncertainty and external disturbance, which has theoretical and practical value.

Integration of Neural Networks and Cellular Automata for Urban Planning

This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey can generate alternative urban growth. Alternative development patterns can b e formed by using different sets of parameter values in CA simulation. A critica l issue is how to define parameter values for realistic and idealized simulation . This paper demonstrates that neural networks can simplify CA models but genera te more plausible results. The simulation is based on a simple three-layer netw ork with an output neuron to generate conversion probability. No transition rule s are required for the simulation. Parameter values are automatically obtained f rom the training of network by using satellite remote sensing data. Original tra ining data can be assessed and modified according to planning objectives. Altern ative urban patterns can be easily formulated by using the modified training dat a sets rather than changing the model.

Adaptive Control of Flexible Redundant Manipulators Using Neural Networks

An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted. The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors. A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator. The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced. The neuro-identifier and the neuro-controller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC. By adjusting the neuro-identifier and the neuro-controller alternatively, the manipulator was controlled on line for achieving the desired dynamic performance. Finally, a planar 3R redundant manipulator with one smart link was utilized as an illustrative example. The simulation results proved the validity of the control strategy.

Towards a Dynamic Controller Scheduling-Timing Problem in Software-Defined Networking

Controller vulnerabilities allow malicious actors to disrupt or hijack the Software-Defined Networking. Traditionally, it is static mappings between the control plane and data plane. Adversaries have plenty of time to exploit the controller's vulnerabilities and launch attacks wisely. We tend to believe that dynamically altering such static mappings is a promising approach to alleviate this issue, since a moving target is difficult to be compromised even by skilled adversaries. It is critical to determine the right time to conduct scheduling and to balance the overhead afforded and the security levels guaranteed. Little previous work has been done to investigate the economical time in dynamic-scheduling controllers. In this paper, we take the first step to both theoretically and experimentally study the scheduling-timing problem in dynamic control plane. We model this problem as a renewal reward process and propose an optimal algorithm in deciding the right time to schedule with the objective of minimizing the long-term loss rate. In our experiments, simulations based on real network attack datasets are conducted and we demonstrate that our proposed algorithm outperforms given scheduling schemes.

Dynamics and Mechanism of A Quorum Sensing Network Regulated by Small RNAs in Vibrio Harveyi

Bacterial quorum sensing （QS） has attracted much interests and it is an important process of cell communication. Recently, Bassler et al. studied the phenomena of QS regulated by small RNAs and the experimental data showed that smafl RNAs played important role in the QS of Vibrio harveyi and it can permit the fine-tuning of gene regulation and mmntenance of homeostasis. According to Michaelis-Menten kinetics and mass action law in this paper, we construct a mathematical model to investigate the mechanism induced QS by coexist of small RNA and signal molecular （AI） and show that there are periodic oscillation when the time delay and Hill coefficient exceed a critical value and the periodic oscillation produces the change of concentration and induces QS. These results are fit to the experimental results. In the meanwhile, we also get some theoretical value of Hopf Bifurcation on time deday. In addition, we also find this network is robust against noise.

Backstepping sliding mode control with self recurrent wavelet neural network observer for a novel coaxial twelve-rotor UAV

The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied. Firstly,a dynamical and kinematical model for the coaxial twelve-rotor UAV is designed. Considering model uncertainties and external disturbances,a robust backstepping sliding mode control( BSMC) with self recurrent wavelet neural network( SRWNN) method is proposed as the attitude controller for the coaxial twelve-rotor. A combinative algorithm of backstepping control and sliding mode control has simplified design procedures with much stronger robustness benefiting from advantages of both controllers. SRWNN as the uncertainty observer is able to estimate the lumped uncertainties effectively.Then the uniformly ultimate stability of the twelve-rotor system is proved by Lyapunov stability theorem. Finally,the validity of the proposed robust control method adopted in the twelve-rotor UAV under model uncertainties and external disturbances are demonstrated via numerical simulations and twelve-rotor prototype experiments.

Free radical reaction model for n-pentane pyrolysis

A mathematical mechanism of the n-pentane pyrolysis process based on free radical reaction model was presented.The kinetic parameters of n-pentane pyrolysis are obtained by quantum chemistry and the reaction network is established. The solution of the stiff ordinary differential equations in the n-pentane pyrolysis model is completed by semi implicit Eular algorithm. Then the pyrolysis mechanism based on free radical reaction model is built,and the computational efficiency increases 10 times by algorithm optimization. The validity of this model and its solution method is confirmed by the experimental results of n-pentane pyrolysis.

Passive control of a 4-scroll chaotic system

This paper studies the control of a new chaotic system which can generate 4-scroll attractors. Based on the properties of a passive system, it derives the essential conditions under which this new chaotic system could be equivalent to a passive system and globally asymptotically stabilize at a zero equilibrium point via smooth state feedback. Simulation results and circuit experiment show that the proposed chaos control method is effective.

Underwater hydraulic shock shovel control system

The control system determines the effectiveness of an underwater hydraulic shock shovel. This paper begins by analyzing the working principles of these shovels and explains the importance of their control systems. A new type of control system’s mathematical model was built and analyzed according to those principles. Since the initial control system’s response time could not fulfill the design requirements, a PID controller was added to the control system. System response time was still slower than required, so a neural network was added to nonlinearly regulate the proportional element, integral element and derivative element coefficients of the PID controller. After these improvements to the control system, system parameters fulfilled the design requirements. The working performance of electrically-controlled parts such as the rapidly moving high speed switch valve is largely determined by the control system. Normal control methods generally can’t satisfy a shovel’s requirements, so advanced and normal control methods were combined to improve the control system, bringing good results.

Equilibrating Pressure with Auto-Control Valve in Water Distribution Networks

This paper probes into the feasibility of equilibrating pressure of water distribution network through (using auto-control valves) in theory and in the economy. An optimal valve control model is designed to minimize the sum of squares of residual pressure in the network. Such an analog simulation has been performed on the year 2003 programming network of a certain littoral district in North China, through which this paper confirms that it is feasible to equilibrate pressure with auto-control valves in theory. The research work has brought about a discovery of applicable conditions of valves in economic feasibility, which avoids a great economic loss due to the wild use of auto-control valve. In addition, simulated annealing algorithm is applied to optimize valve settings and shown to identify global optimum or near-optimum.

Hybrid internal model control and proportional control of chaotic dynamical systems

A new chaos control method is proposed to take advantage of chaos or avoid it. The hybrid Internal Model Control and Proportional Control learning scheme are introduced. In order to gain the desired robust performance and ensure the system's stability, Adaptive Momentum Algorithms are also developed. Through properly designing the neural network plant model and neural network controller, the chaotic dynamical systems are controlled while the parameters of the BP neural network are modified. Taking the Lorenz chaotic system as example, the results show that chaotic dynamical systems can be stabilized at the desired orbits by this control strategy.

Network Traffic Signature Generation Mechanism Using Principal Component Analysis

The Deep Packet Inspection(DPI)method is a popular method that can accurately identify the flow data and its corresponding application.Currently,the DPI method is widely used in common network management systems.However,the major limitation of DPI systems is that their signature library is mainly extracted manually,which makes it hard to efficiently obtain the signature of new applications.Hence,in this paper,we propose an automatic signature extraction mechanism using Principal Component Analysis(PCA)technology,which is able to extract the signature automatically.In the proposed method,the signatures are expressed in the form of serial consistent sequences constructed by principal components instead of normally separated substrings in the original data extracted from the traditional methods.Extensive experiments based on numerous sets of data have been carried out to evaluate the performance of the proposed scheme,and the results prove that the newly proposed method can achieve good performance in terms of accuracy and efficiency.