Optimal position and velocity navigation filters for autonomous vehicles

This paper presents the design and performance evaluation of a set of globally asymptotically stable time-varying kinematic filters with application to the estimation of linear motion quantities of mobile platforms (position, linear velocity, and acceleration) in three dimensions. The proposed techniques are based on the Kalman and H_∞ optimal filters for linear time-varying systems and the explicit optimal filtering solutions are obtained through the use of an appropriate coordinate transformation, whereas the design employs frequency weights to achieve adequate disturbance rejection and attenuation of the measurement noise on the state estimates. Two examples of application in the field of ocean robotics are presented that demonstrate the potential and usefulness of the proposed design methodology. In the first the proposed filtering solutions allow for the design of a complementary navigation filter for the estimation of unknown constant ocean currents, while the second addresses the problem of estimation of the velocity of an underwater vehicle, as well as the acceleration of gravity. Simulation results are included that illustrate the filtering achievable performance in the presence of both extreme environmental disturbances and realistic measurement noise.     

基于多体动力学的车辆道岔通过性能研究

道岔复杂的轮轨关系及其变截面特性是车辆通过道岔时引起振动甚至脱轨的关键因素.根据60kg/m钢轨18号可动心轨道岔设计布置图,在多体动力学软件中建立车辆—道岔耦合系统模型,在此基础上对车辆—道岔系统模型进行验证,仿真计算车辆侧向和直向通过道岔的动力学响应.结果表明转辙器区、辙叉区轨道截面变化和轮轨型面匹配是影响车辆动力学性能的主要因素.最后,对车辆侧向通过离散轨道模型工况下的动力学响应进行仿真计算,讨论道岔轨下整体刚度和阻尼对模型动力学性能的影响,为改善车辆通过道岔时的动力学性能、道岔轨下刚度与阻尼参数匹配提供理论基础.     

基于数据融合模型的网络安全分析评估系统

在借鉴网络性能监控模型的基础上,该文基于数据融合技术提出了一个网络安全分析与评估的分层实现模型,分析了网络系统中存在的脆弱性,采用安全案例表示与贝叶斯推理相结合的方法对网络安全状况进行建模和分析,并阐述了模型的实现要点。     

Adaptive control for autonomous ships with uncertain model and unknown propeller dynamics

Motion control is one of the most critical aspects in the design of autonomous ships. During maneuvering, the dynamics of propellers as well as the craft hydrodynamical specifications experience severe uncertainties. In this paper, an adaptive control approach is proposed to control the motion and trajectory tracking of an autonomous vessel by adopting neural networks that is used for estimating the dynamics of the propellers and handling hydrodynamical uncertainties. Considering that the maneuvering model of a vessel resemble a nonlinear non-affine-in-control system, the proposed neural-based adaptive control algorithm is designed to estimate the nonlinear influence of the input function which in this case is the dynamics of propellers and thrusters. It is also shown that the proposed methodology is capable of handling state dependent uncertainties within the ship maneuvering model. A Lyapunov-based technique and Uniform Ultimate Boundedness are used to prove the correctness of the algorithm. To assess the method’s performance, several experiments are considered including trajectory tracking simulations in the port of Rotterdam.     

汽车横摆的动态模型

建立横摆动态模型是汽车横摆稳定控制的基础．本文根据汽车横摆稳定控制的特点,综合车身横摆动态,车轮动态和轮胎的非线性特性,建立了非线性离散横摆动态模型,其中轮胎摩擦力为时变参数．为了估计本模型中的时变参数,本文进而设计了离散滑模估计器估计轮胎摩擦力．最后,在一个高精度的汽车动力学仿真环境中,证实了本估计方法可有效地估计车轮摩擦力,所建立模型较为准确地反映汽车横摆动态．     

Development of a real-time control architecture for a semi-autonomous underwater vehicle for intervention missions

The need for autonomous underwater vehicles (AUVs) for intervention missions becomes greater as they can perform underwater tasks requiring physical contacts with the underwater environment, such as underwater plug-in/plug-out, construction and repair, cable streaming, mine hunting, munitions retrieval, and scientific sampling. This paper describes a semi-autonomous underwater vehicle for intervention missions that has multiple on-board CPUs, redundant sensors and actuators, on-board power source and a robotic manipulator for dextrous underwater performance. Such a complex robotic vehicle system requires advanced control software architecture for on-board intelligence with a wide range of sensors and actuators to carry out required missions. In this paper, AUV control architectures are reviewed and a sensor data bus based control architecture (SDBCA) is presented. SDBCA is a modified hierarchical architecture that offers good controllability and stability while sensor data bus increases flexibility of system design, making it possible to have a prompt response from high-level control with respect to low-level sensor data. The overall sensor input mechanism of SDBCA becomes similar to the sensor input mechanism of subsumption architecture.     

Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle

In this paper, optimal three-dimensional paths are generated offline for waypoint guidance of a miniature Autonomous Underwater Vehicle (AUV). Having the starting point, the destination point, and the position and dimension of the obstacles, the AUV is intended to systematically plan an optimal path toward the target. The path is defined as a set of waypoints to be passed by the vehicle. Four criteria are considered for evaluation of an optimal path; they are “total length of path”, “margin of safety”, “smoothness of the planar motion” and “gradient of diving”. A set of Pareto-optimal solutions is found where each solution represents an optimal feasible path that cannot be outrun by any other path considering all four criteria. Then, a proposed three-dimensional guidance system is used for guidance of the AUV through selected optimal paths. This system is inspired from the Line-of-Sight (LOS) guidance strategy; the idea is to select the desired depth, presumed proportional to the horizontal distance of the AUV and the target. To develop this guidance strategy, the dynamic modeling of this novel miniature AUV is also derived. The simulation results show that this guidance system efficiently guides the AUV through the optimal paths.     

Adaptive tracking control of an autonomous underwater vehicle

This paper presents the trajectory tracking control of an autonomous underwater vehicle(AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to track the desired trajectory. This desired state-dependent regressor matrix-based controller provides consistent results under hydrodynamic parametric uncertainties.Stability of the developed controller is verified using the Lyapunov s direct method. Numerical simulations are carried out to study the efficacy of the proposed adaptive controller.     

Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties

This paper addresses a three-dimensional (3D) path following control problem for underactuated autonomous underwater vehicle (AUV) subject to both internal and external uncertainties. A two-layered framework synthesizing the 3D guidance law and heuristic fuzzy control is proposed to achieve robust adaptive following along a predefined path. In the first layer, a 3D guidance controller for underactuated AUV is presented to guarantee the stability of path following in the kinematics stage. In the second layer, a heuristic adaptive fuzzy algorithm based on the guidance command and feedback linearization Proportional-Integral-Derivative (PID) controller is developed in the dynamics stage to account for the nonlinear dynamics and system uncertainties, including inaccuracy modelling parameters and time-varying environmental disturbances. Furthermore, the sensitivity analysis of the heuristic fuzzy controller is presented. Against most existing methods for 3D path following, the proposed robust fuzzy control scheme reduces the design and implementation costs of complicated dynamics controller, and relaxes the knowledge of the accuracy dynamics modelling and environmental disturbances. Finally, numerical simulation results validate the effectiveness of the proposed control framework and illustrate the outperformance of the proposed controller as well.     

Decentralized estimation and control of graph connectivity for mobile sensor networks

The ability of a robot team to reconfigure itself is useful in many applications: for metamorphic robots to change shape, for swarm motion towards a goal, for biological systems to avoid predators, or for mobile buoys to clean up oil spills. In many situations, auxiliary constraints, such as connectivity between team members or limits on the maximum hop-count, must be satisfied during reconfiguration. In this paper, we show that both the estimation and control of the graph connectivity can be accomplished in a decentralized manner. We describe a decentralized estimation procedure that allows each agent to track the algebraic connectivity of a time-varying graph. Based on this estimator, we further propose a decentralized gradient controller for each agent to maintain global connectivity during motion.     

A new approach to vision-based unsupervised learning of unexplored indoor environment for autonomous land vehicle navigation

A vision-based approach to unsupervised learning of the indoor environment for autonomous land vehicle (ALV) navigation is proposed. The ALV may, without human's involvement, self-navigate systematically in an unexplored closed environment, collect the information of the environment features, and then build a top-view map of the environment for later planned navigation or other applications. The learning system consists of three subsystems: a feature location subsystem, a model management subsystem, and an environment exploration subsystem. The feature location subsystem processes input images, and calculates the locations of the local features and the ALV by model matching techniques. To facilitate feature collection, two laser markers are mounted on the vehicle which project laser light on the corridor walls to form easily detectable line and corner features. The model management subsystem attaches the local model into a global one by merging matched corner pairs as well as line segment pairs. The environment exploration subsystem guides the ALV to explore the entire navigation environment by using the information of the learned model and the current ALV location. The guidance scheme is based on the use of a pushdown transducer derived from automata theory. A prototype learning system was implemented on a real vehicle, and simulations and experimental results in real environments show the feasibility of the proposed approach.     

JSAE-SICE benchmark problem for vehicle dynamics control

Recently many new types of small vehicles for future urban societies have been proposed and developed. Such small vehicles tends to have reduced stability and handling ability than conventional vehicles because of their lighter weight and reduced tire performance. To cope with this problem by active collaboration of Japanese academia and industries, a benchmark problem of designing vehicle control logic for an articulated In-Wheel-Motor vehicle was settled by Japanese society of automotive industries and academia. For this purpose, simulation models of the new vehicle using multi-physics acausal modeling language Modelica were provided from the industry side. Challengers were requested to design controllers of tire steering angle, tire camber angle and tire driving force to satisfy requested vehicle dynamic characteristics. There also were some restrictions about the range of actuators. Four test scenarios were given to evaluate the control performance. Many challengers from Japanese Universities have tackled with this benchmark problem. Some results of their researches are also introduced in this paper.     

自治网络中信任信誉模型的安全现状研究

随着P2P网络、Ad hoc、无线传感器网络的深入研究,信任和信誉成为保障这类自治网络安全的一个重要手段.虽然信任信誉系统在自治网络中起到了重要的作用,但其采用了间接推荐等技术,给信任信誉带来很多安全问题.介绍了信任信誉模型的相关概念,总结了目前对信任信誉模型的新攻击手段,并针对这些攻击,比较分析了在自治网络环境中具备一定防御能力的典型信任信誉模型的各自防御方法、防御效果以及性能情况.最后,在分析了现有研究存在的主要问题的基础上,展望了今后提高信任信誉模型安全性研究的主要方向.     

一种基于粗糙集理论的网络安全态势感知方法

网络安全态势感知是目前网络安全领域研究的一个热点问题.本文提出利用粗糙集理论进行网络安全态势感知,该方法把网络攻击行为作为网络安全要素,定量分析了各安全要素或安全要素组合对网络安全的威胁程度,最终建立了具有攻击行为、网络服务和安全态势3个层次的网络安全态势感知模型,并通过仿真实验生成了明确的网络安全态势图.采用粗糙集理论使得系统能够高效处理海量网络安全状态数据,生成易理解的攻击检测规则,清楚直观地反映攻击行为对网络整体安全状况的影响.     

浅谈计算机网络安全的具体问题及控制策略

随着我国科技水平的不断提高,计算机技术也逐渐被应用到我们的日常生活当中,极大地丰富了我们的日常生活内容,也带来了更多的信息资源,促进了社会主义精神文明的快速发展,计算机的安全问题也越来越受到人们的重视,在对计算机的网络系统进行设计的时候一定要充分考虑到计算机网络系统设计的稳定性和规范性,保障计算机的运行安全,采用先进的技术手段,制定相应的解决措施,保障我国计算机应用的安全性,促进我国计算机信息安全产业的快速发展。     

高校网络系统的安全管理策略

分析了高校网络系统安全的现状，建议必须加强内部的安全管理和提高安全技术水平，以规避高校网络系统应用的风险。还从管理和技术两个方面对高校网络系统的安全体系作了进一步论述，并提出了高校网络系统的安全策略、安全设计的原则和措施。     

ACL技术在网络安全中的应用

访问控制列表(ACL)被广泛应用在路由器和三层交换机上,它是用来过滤和控制数据包的一种访问控制技术。该文主要阐述了访问控制列表的基本概念、主要功能、工作过程及配置,并以H3C路由器为例说明了访问控制列表在网络安全方面的具体应用,同时给出了ACL的关键配置代码。     

效用理论在信息安全投资优化中的应用

为了解决组织预算过程中信息安全投资最优的问题,建立了安全投资与风险控制的关系模型,对安全投资的有效性进行了研究,提出了降低事件发生概率有效性及缓解损失有效性的新概念.采用效用理论作为组织财富、风险损失和安全投资的描述模型,指数效用函数作为组织投资收益的描述模型.分析了安全投资的边界,使用求偏导数取极值的方法对投资效用函数进行了研究,并求得最小投资的解.应用实例表明,基于效用的风险度量方法是科学的,损失效应越大的安全事件需要更大的安全投资.     

Dimensional analysis for robust control of planar vehicle dynamics

Most engineered systems with similar functions, which are designed under strict external constraints, share similar dynamics. Using a dimensionless model as system representation, a dimensionless robust controller can be designed and implemented for a class of dynamically similar systems that are different in physical dimension. Dimensionless transformations of timescale, inputs and outputs determine a nominal plant model and plant‐to‐plant uncertainties in a dimensionless form. Using parameter‐dependent normalization, a normalized dimensionless model can be derived that has lower levels of plant‐to‐plant uncertainty than previous formulations (IEEE Trans. Contr. Syst. Technol. 2005; 13 (4):624–630). The benefit of this dimensional analysis is demonstrated by the dynamic analysis of different types of planar vehicle systems. Numerical results show that, by using dimensional analysis, it is possible to obtain a controller that is robust to plant‐to‐plant parametric uncertainties among specific classes of systems designed with widely varying physical dimensions. Copyright © 2007 John Wiley & Sons, Ltd.     

Robust online roll dynamics identification of a vehicle using sliding mode concepts

This paper proposes a robust observer concept for joint estimation of system states and model parameters related to the roll dynamics of a vehicle. Using sliding mode concepts introduces robustness to parametric uncertainties and also allows reconstruction of the latter. These model parameters are of interest for vehicle dynamics assessment and estimation of the roll angle. A novel classification concept exploits these parameter estimates for assessing the roll dynamics. An additional benefit of the proposed method is the minimal requirement of measurement equipment as it only relies on cost-efficient angular rate and acceleration sensors. Evaluation of the framework is performed in simulations and real-world using an experimental vehicle.