波浪作用下船舶航向自抗扰控制设计及参数配置

针对具有内部参数不确定性和外部扰动的海上船舶设计了航向自抗扰控制器,并解决了舵机模型中舵角的限幅和限速问题,基于滑模控制理论提出了反馈控制带宽的计算方法.采用频域分析的方法,系统地分析了自抗扰控制器对外部波浪扰动的抑制能力、模型参数不确定时的鲁棒性;结合作者实船工作经验以及系统动态特性与控制参数的关系,提出了船舶航向控制器参数的配置规律;最后以一艘57000吨级散货船为控制对象,验证了航向控制器的鲁棒性和本文所述参数配置规律的有效性.为将自抗扰控制算法应用于船舶自动舵设计提供理论依据和实践参考.     

Learning control of mobile robots using a multiprocessor system

A real-time multiprocessor system is proposed for the solution of the tracking problem of mobile robots operating in a real context with environmental disturbances and parameter uncertainties. The proposed control scheme utilizes multiple models of the robot for its identification in an adaptive and learning control framework. Radial Basis Function Networks (RBFNs) are considered for the multiple models in order to exploit the net non-linear approximation capabilities for modeling the kinematic behavior of the vehicle and for reducing unmodeled contributions to tracking errors. The training of the nets and the tests of the achieved control performance have been done in a real experimental setup. The proposed control architecture improves the robot tracking performance achieving fast and accurate control actions in presence of large and time-varying uncertainties in dynamical environments. The experimental results are satisfactory in terms of tracking errors and computational efforts.     

On the learning of rule uncertainties and their integration into probabilistic knowledge bases

We present a natural and realistic knowledge acquisition and processing scenario. In the first phase a domain expert identifies deduction rules that he thinks are good indicators of whether a specific target concept is likely to occur. In a second knowledge acquisition phase, a learning algorithm automatically adjusts, corrects and optimizes the deterministic rule hypothesis given by the domain expert by selecting an appropriate subset of the rule hypothesis and by attaching uncertainties to them. Then, in the running phase of the knowledge base we can arbitrarily combine the learned uncertainties of the rules with uncertain factual information.Formally, we introduce the natural class of disjunctive probabilistic concepts and prove that this class is efficiently distribution-free learnable. The distribution-free learning model of probabilistic concepts was introduced by Kearns and Schapire and generalizes Valiant's probably approximately correct learning model. We show how to simulate the learned concepts in probabilistic knowledge bases which satisfy the laws of axiomatic probability theory. Finally, we combine the rule uncertainties with uncertain facts and prove the correctness of the combination under an independence assumption.     

车辆线控转向系统的分数阶鲁棒控制研究

线控转向系统有别于普通车辆的机械转向机构,它是由转向电机、转向机构、转角和力矩传感器以及控制单元构成的复杂转向系统．系统性能受到参数的不确定性、未建模动态以及前轮回正力矩的影响．本文基于分数微积分理论,根据转向系统鲁棒性的设计要求,提出一种新的PI^λD^μ控制策略,保证线控转向系统在所要求的频域范围具有鲁棒性．文中通过优化方法得到PI^λD^μ控制器的五个设计参数,并用Oustaloup算法对分数阶控制器进行仿真验证,结果表明该控制方法对提高转向系统性能的鲁棒性是有效的．     

不确定性机器人系统自适应鲁棒迭代学习控制

利用Lyapunov方法, 提出了一种不确定性机器人系统的自适应鲁棒迭代学习控制策略, 整个系统在迭代域里是全局渐近稳定的. 所考虑的机器人系统同时包含了结构和非结构不确定性. 在设计时, 系统的不确定性被分解成可重复性和非重复性两部分, 并考虑了系统的标称模型. 在所提出的控制策略中, 自适应策略用来估算做法确定性的界, 界的修正与迭代学习控制量一样的迭代域得以实现的. 计算机仿真表明本文提出的控制策略是有效的.     

An overview of methods to evaluate uncertainty of deterministic models in decision support

There is an increasing need for environmental management advice that is wide-scoped, covering various interlinked policies, and realistic about the uncertainties related to the possible management actions. To achieve this, efficient decision support integrates the results of pre-existing models. Many environmental models are deterministic, but the uncertainty of their outcomes needs to be estimated when they are utilized for decision support. We review various methods that have been or could be applied to evaluate the uncertainty related to deterministic models' outputs. We cover expert judgement, model emulation, sensitivity analysis, temporal and spatial variability in the model outputs, the use of multiple models, and statistical approaches, and evaluate when these methods are appropriate and what must be taken into account when utilizing them. The best way to evaluate the uncertainty depends on the definitions of the source models and the amount and quality of information available to the modeller.     

Expert systems applied to problems in geographic information systems: Introduction, review and prospects

This paper critically reviews current expert system developments relevant to geographic information systems, and identifies several research topics for application of expert system technology in geographic information systems. We have identified four major problem domains of geographic information systems in which expert system technology has been applied—automated map design, terrain/feature extraction, database management/user interface, and geographic decision support systems. Efforts in each problem domain are critically reviewed. Considering the accomplishments and shortcomings of efforts to date, we identify areas likely to gain importance in this field. Our view of these prospects is moderated by constraints of current technology and a realistic view of current efforts. Future research will place more emphasis on formal representation of both knowledge and uncertainty. Another future research area will be the development of advanced tools for geographic knowledge acquisition. Finally, better methods for working with large-scale geographic databases will be needed.     

Fault‐tolerant path‐following control for in‐wheel‐motor‐driven autonomous ground vehicles with differential steering

Over the past several decades, the automobile industry has denoted significant research efforts to developing in‐wheel‐motor‐driven autonomous ground vehicles (IWM‐AGVs) with active front‐wheel steering. One of the most fundamental issues for IWM‐AGVs is path following, which is important for automated driving to ensure that the vehicle can track a target‐planned path during local navigation. However, the path‐following task may fail if the system experiences a stuck fault in the active front‐wheel steering. In this paper, a fault‐tolerant control (FTC) strategy is presented for the path following of IWM‐AGVs in the presence of a stuck fault in the active front‐wheel steering. For this purpose, differential steering is used to generate differential torque between the left and right wheels in IWM‐AGVs, and an adaptive triple‐step control approach is applied to realize coordinated lateral and longitudinal path‐following maneuvering. The parameter uncertainties for the cornering stiffness and external disturbances are considered to make the vehicles robust to different driving environments. The effectiveness of the proposed scheme is evaluated with a high‐fidelity and full‐car model based on the veDYNA‐Simulink joint platform.     

基于力矩信息的拖拉机转向动态PID控制方法

针对拖拉机驾驶机器人自主转向系统在转向过程中的阻尼具有非线性和时变性的特点,同时为了提高转向系统的控制性能,设计了基于力矩信号检测的拖拉机转向控制系统,其由力矩传感器、STM32处理器、工控机、驱动器及直流电机等组成.首先通过仿真研究不同力矩时的最优PID控制参数,给出不同力矩时的最优PID参数规律,再通过力矩传感器检测的力矩信号动态调整PID参数来控制电机的转向.仿真及实验结果均表明,基于力矩信号反馈的拖拉机动态PID转向控制方法能够有效控制拖拉机的方向,响应快、超调小、适应性强.     

A Logical Architecture for Active Network Management

This paper focuses on improving network management by exploiting the potential of “doing” of the Active Networks technology, together with the potential of “planning,” which is typical of the artificial intelligent systems. We propose a distributed multiagent architecture for Active Network management, which exploits the dynamic reasoning capabilities of the Situation Calculus in order to emulate the reactive behavior of a human expert to fault situations. The information related to network events is generated by programmable sensors deployed across the network. A logical entity collects this information, in order to merge it with general domain knowledge, with a view to identifying the root causes of faults, and to deciding on reparative actions. The logical inference system has been devised to carry out automated isolation, diagnosis, and even repair of network anomalies, thus enhancing the reliability, performance, and security of the network. Experimental results illustrate the Reasoner capability of correctly recognizing fault situations and undertaking management actions.     

Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning

Numerical weather forecasts, such as meteorological forecasts of precipitation, are inherently uncertain. These uncertainties depend on model physics as well as initial and boundary conditions. Since precipitation forecasts form the input into hydrological models, the uncertainties of the precipitation forecasts result in uncertainties of flood forecasts. In order to consider these uncertainties, ensemble prediction systems are applied. These systems consist of several members simulated by different models or using a single model under varying initial and boundary conditions. However, a too wide uncertainty range obtained as a result of taking into account members with poor prediction skills may lead to underestimation or exaggeration of the risk of hazardous events. Therefore, the uncertainty range of model-based flood forecasts derived from the meteorological ensembles has to be restricted.In this paper, a methodology towards improving flood forecasts by weighting ensemble members according to their skills is presented. The skill of each ensemble member is evaluated by comparing the results of forecasts corresponding to this member with observed values in the past. Since numerous forecasts are required in order to reliably evaluate the skill, the evaluation procedure is time-consuming and tedious. Moreover, the evaluation is highly subjective, because an expert who performs it makes his decision based on his implicit knowledge.Therefore, approaches for the automated evaluation of such forecasts are required. Here, we present a semi-automated approach for the assessment of precipitation forecast ensemble members. The approach is based on supervised machine learning and was tested on ensemble precipitation forecasts for the area of the Mulde river basin in Germany. Based on the evaluation results of the specific ensemble members, weights corresponding to their forecast skill were calculated. These weights were then successfully used to reduce the uncertainties within rainfall-runoff simulations and flood risk predictions.     

A flexible extreme value mixture model

Extreme value theory is used to derive asymptotically motivated models for unusual or rare events, e.g. the upper or lower tails of a distribution. A new flexible extreme value mixture model is proposed combining a non-parametric kernel density estimator for the bulk of the distribution with an appropriate tail model. The complex uncertainties associated with threshold choice are accounted for and new insights into the impact of threshold choice on density and quantile estimates are obtained. Bayesian inference is used to account for all uncertainties and enables inclusion of expert prior information, potentially overcoming the inherent sparsity of extremal data. A simulation study and empirical application for determining normal ranges for physiological measurements for pre-term infants is used to demonstrate the performance of the proposed mixture model. The potential of the proposed model for overcoming the lack of consistency of likelihood based kernel bandwidth estimators when faced with heavy tailed distributions is also demonstrated.     

基于功率键合图装载机转向系统建模与仿真

为了使装载机转向系统的设计能够保证在作业或行驶时的机动性、安全性 和敏捷性,一般要对该系统进行虚拟仿真设计。首先分析小型多功能装载机液压转向系统工 作特性,包括系统组成,转向工作原理,及系统模型简化;然后以键合图理论为建模工具, 根据能量和信息流动与转换过程的不同,将转向系统各组成元件抽象为键合图元,建立了液 压转向系统的功率键合图;接着推导了液压转向系统工作过程的状态方程;最后,在 MWORKS 软件环境中建立了液压转向系统功仿真模型,并对转向过程进行了仿真测试。为 小型多功能装载机转向系统设计制提供了一套完整的理论与方法,对装载机转向系统的设计 具有着重要的理论和工程应用价值。     

基于驾驶状态预测的人机力矩协同转向控制器设计

针对人机协同转向控制中对于驾驶员参与和驾驶员状态考虑较少这一问题,提出一种基于驾驶员状态预测的人机力矩协同(human-vehicle torque collaborative based on driver state prediction,HVTC-DSP)转向控制方法.该方法以力矩为人机交互接口,提高了驾驶员的参与程度;同时,在控制器设计过程中采用模型预测控制方法,将驾驶员状态考虑在内,对驾驶员状态进行预测.采用高精度车辆仿真软件veDYNA进行仿真验证,结果表明,与不考虑驾驶员状态的人机协同力矩(human-vehicle torque collaborative based on no driver state prediction,HVTC-NDSP)转向控制方法相比,所提方法可以使辅助力矩更好地跟随驾驶员动作,提高车辆转向性能,减小侧向位移偏差,同时对不同驾驶员也有较好的适应性.进而,以驾驶员下一步动作为参考,使驾驶员当前力矩尽可能接近下一步期望的力矩,在转向性能几乎不受影响的情况下,适当减轻驾驶员操作负担.     

Baseball Timeline: Summarizing Baseball Plays Into a Static Visualization

In sports, Play Diagrams are the standard way to represent and convey information. They are widely used by coaches, managers, journalists and fans in general. There are situations where diagrams may be hard to understand, for example, when several actions are packed in a certain region of the field or there are just too many actions to be transformed in a clear depiction of the play. The representation of how actions develop through time, in particular, may be hardly achieved on such diagrams. The time, and the relationship among the actions of the players through time, is critical on the depiction of complex plays. In this context, we present a study on how player actions may be clearly depicted on 2D diagrams. The study is focused on Baseball plays, a sport where diagrams are heavily used to summarize the actions of the players. We propose a new and simple approach to represent spatiotemporal information in the form of a timeline. We designed our visualization with a requirement driven approach, conducting interviews and fulfilling the needs of baseball experts and expert‐fans. We validate our approach by presenting a detailed analysis of baseball plays and conducting interviews with four domain experts.     

Finite-tree analysis for constraint logic-based languages

Logic languages based on the theory of rational, possibly infinite, trees have much appeal in that rational trees allow for faster unification (due to the safe omission of the occurs-check) and increased expressivity (cyclic terms can provide very efficient representations of grammars and other useful objects). Unfortunately, the use of infinite rational trees has problems. For instance, many of the built-in and library predicates are ill-defined for such trees and need to be supplemented by run-time checks whose cost may be significant. Moreover, some widely used program analysis and manipulation techniques are correct only for those parts of programs working over finite trees. It is thus important to obtain, automatically, a knowledge of the program variables (the finite variables) that, at the program points of interest, will always be bound to finite terms. For these reasons, we propose here a new data-flow analysis, based on abstract interpretation, that captures such information. We present a parametric domain where a simple component for recording finite variables is coupled, in the style of the open product construction of Cortesi et al., with a generic domain (the parameter of the construction) providing sharing information. The sharing domain is abstractly specified so as to guarantee the correctness of the combined domain and the generality of the approach. This finite-tree analysis domain is further enhanced by coupling it with a domain of Boolean functions, called finite-tree dependencies, that precisely captures how the finiteness of some variables influences the finiteness of other variables. We also summarize our experimental results showing how finite-tree analysis, enhanced with finite-tree dependencies, is a practical means of obtaining precise finiteness information.     

DYNAMIC ANALYSES AND ROBUST STEERING CONTROLLER DESIGN FOR AUTOMATED LANE GUIDANCE OF HEAVY‐DUTY VEHICLES

In this paper, we present various linear analyses of the linearized lateral dynamics of heavy‐duty vehicles (HDVs) (tractor‐semitrailer type), which include time domain, frequency domain and pole/zero analyses. These analyses are conducted to examine the vehicle response to the steering input subjected to variations of speed, road adhesion coefficient, cargo load in the trailer, and look‐ahead distance for the lateral deviation sensor. These parameters (uncertainties) have significant influence on vehicle dynamics. It has been shown that redefining the look‐ahead lateral error as the controlled output has a favorable impact on the lateral control problem. Based on these analyses, a robust steering controller using H_∞ loop‐shaping procedure is designed for a tractor semitrailer combination to follow the road center line on both curved and straight highway sections. The proposed controller ensures the robust performance under model uncertainties which include varying vehicle longitudinal speed, road adhesion coefficient, and cargo load in the trailer. The performance of the designed controller is evaluated by simulations and validated by experiments.     

A model of software design

In this article we present a preliminary cognitive model of the process of software design. Our goal was to develop a model of expert problem-solving skills for a task in which domain knowledge played an extensive role. In our model the process of design is captured via goals-and-operators interacting with a knowledge base. We have defined the goals and operators as ones which are general to design, rather than specific to the current task. In addition, we have structured the atomic level operators so that they are able to access domain specific knowledge acquired through experience. This structure enables both general processes and domain specific knowledge to play critical roles in producing any particular design artifact. From our protocol analysis we have built a model which unites several recurring behaviors into an interpretable whole. the behaviors we account for include the building of mental models, mental simulation, and balanced development.     

World lines

In this paper we present World Lines as a novel interactive visualization that provides complete control over multiple heterogeneous simulation runs. In many application areas, decisions can only be made by exploring alternative scenarios. The goal of the suggested approach is to support users in this decision making process. In this setting, the data domain is extended to a set of alternative worlds where only one outcome will actually happen. World Lines integrate simulation, visualization and computational steering into a single unified system that is capable of dealing with the extended solution space. World Lines represent simulation runs as causally connected tracks that share a common time axis. This setup enables users to interfere and add new information quickly. A World Line is introduced as a visual combination of user events and their effects in order to present a possible future. To quickly find the most attractive outcome, we suggest World Lines as the governing component in a system of multiple linked views and a simulation component. World Lines employ linking and brushing to enable comparative visual analysis of multiple simulations in linked views. Analysis results can be mapped to various visual variables that World Lines provide in order to highlight the most compelling solutions. To demonstrate this technique we present a flooding scenario and show the usefulness of the integrated approach to support informed decision making.