Decentralized overlapping control of a formation of unmanned aerial vehicles

Decentralized overlapping feedback laws are designed for a formation of unmanned aerial vehicles. The dynamic model of the formation with an information structure constraint in which each vehicle, except the leader, only detects the vehicle directly in front of it, is treated as an interconnected system with overlapping subsystems. Using the mathematical framework of the inclusion principle, the interconnected system is expanded into a higher dimensional space in which the subsystems appear to be disjoint. Then, at each subsystem, a static state feedback controller is designed to robustly stabilize the perturbed nominal dynamics of the subsystem. The design procedure is based on the application of convex optimization tools involving linear matrix inequalities. As a final step, the decentralized controllers are contracted back to the original interconnected system for implementation.     

DESIGN OF REDUCED‐ORDER FUNCTIONAL OBSERVERS FOR LINEAR SYSTEMS WITH UNKNOWN INPUTS

This brief paper presents new conditions for the existence and design of reduced‐order linear functional state observers for linear systems with unknown inputs. Systematic procedures for the synthesis of reduced‐order functional observers are given. Numerical examples are given to illustrate the attractiveness and simplicity of the new design procedures.     

不完备信息系统中一个变精度粗糙集模型

针对不完备信息系统的知识获取,提出了一种基于相容度的变精度粗糙集模型,该模型是变精度粗糙集模型在不完备信息系统中的拓展.定义了这种模型上的知识约简,并给出了求约简的一个启发式算法.最后通过实例分析说明了算法的有效性.     

Automation, work organization and skills: The case of numerical control

This paper deals with small and medium sized firms. Since the late seventies small and medium sized mechanical engineering firms in France have resorted increasingly to new technologies, and in particular to numerical control. Numerical control has introduced major changes in work organization. On the one hand it tends to reinforce the role of methods of production engineering whatever the type of numerical control may be. Programming is seldom left to the machine operators, except in the case of straightforward machining operations or in machining shops with a strong tradition of highly skilled or artisan labour. Numerical control, on the other hand, does provide more opportunity for collaboration between the workshop and the production engineers. On a workshop level it introduces a more mobile division of labour between programmers, machine setters and operators, which covers everything from the start-up of programs to the supervision of maching operations. The operator may therefore acquire new skills, but this is only possible if there are open-ended training systems available to facilitate the transmission of know-how from the programmers to the machine setters and from the machine setters to the operators, and vice versa. Small and medium sized firms would appear to have a special role to play in developing skills.     

Decentralized adaptive recurrent neural control structure

This paper presents a novel decentralized variable structure neural control approach for large-scale uncertain systems, which is developed using recurrent high-order neural networks (RHONN). It is assumed that each subsystem belongs to a class of block-controllable nonlinear systems whose vector fields includes interconnection terms, which are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law are proposed in order to approximate online the dynamical behavior of each nonlinear subsystem. The control law, which is able to regulate and to track the desired reference signals, is designed using the well-known variable structure theory. The stability of the whole system is analyzed via the Lyapunov methodology. The applicability of the proposed decentralized identification and control algorithm is illustrated via simulations as applied to an interconnected double inverted pendulum.     

基于最优解耦的鲁棒故障检测方法研究

系统地研究了线性系统在模型参数变动和存在外部扰动下的控制系统故障的鲁棒故障检测方法。分析了在与干扰信号不能完全解耦的情况下,如何设计残差信号使其对模型参数变化及噪声不敏感,而对故障敏感,由此得出了对未知输入具有鲁棒性的条件方程式,并基于最优解耦理论,通过矩阵的奇异值分解实现了最优解耦。在给出的仿真实例中,验证了该文提出方法的有效性。     

神经控制（Ⅰ）─—神经元控制

介绍了作者在神经控制──神经元控制方面的研究成果，主要包括面向控制的神经元模型、神经元控制系统、神经元无模型控制方法和应用实例。     

Analyzing the problem of unsustainable health information systems in less-developed economies: Case studies from Tanzania and Mozambique

Most of donor-supported information technology (IT)–based projects developed or implemented in less-developed economies (LDEs) end up as complete or partial failures or unsustainable. Notably, a number of intra-organizational and external factors are associated with this problem, including inadequate infrastructure and human resource capacity, fragmented donor policy, and lack of policies to manage the sustainability problem. Accordingly, IT initiatives are often donor-driven, top-down, and hijacked by top managers who (normally) do not have adequate skills, but have enormous power to enforce such initiatives across organizational hierarchies.

In analyzing the concepts from sustainability and institutionalization, key insights towards a better understanding of the problem of unsustainability are developed. It is argued that health information systems (HISs) become sustainable if they are institutionalized in the sense of being integrated into the everyday routine of the user organization. However, a sustainable HIS should also be flexible enough to allow changes as the user needs change. Moreover, introduction of a new HIS is not only a technical change, but requires the cultivation and institutionalization of a new kind of culture.

Through a comparative case analysis of the HIS development and implementation processes in Tanzania and Mozambique, we have identified two sets of relationships, between the Ministry of Health (MoH) and donor agencies and between the MoH and software development agencies as critical and contributing factors to the unsustainability of a HIS. Given this setting, we highlight three key strategies for dealing with the problem of unsustainability in LDEs: (a) integration of a HIS, (b) local shaping of new cultures, and (c) cultivation approach to systems development. © 2005 Wiley Periodicals, Inc.     

Learning Structure from Data and Its Application to Ozone Prediction

In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters.