Annual Review in Automatic Programming最新文献

A wide variety of industrial systems are controlled and supervised by programmable logic controllers (PLCs). Although the PLC is particularly well adapted to problems requiring sequential control or the verification of interlocks prior to further actions taking place, they are very poor at supporting diagnostics when problems occur. On the other hand, the PLC does contain valuable information to assist with diagnosis though, it is just very difficult for an engineer or operator to understand it directly. This paper describes the automatic development of a troubleshooting system which can be build using the precise and deterministic knowledge embedded within the PLC and extending it with heuristic knowledge.

Human beings are capable of learning to manually control complex nonlinear dynamical systems. It is well known, however, that humans have great difficulty in articulating the rules underlying their skilled behaviour. This paper focusses on the automatic machine induction of control rules from past records of skilled human behaviour. The aim of this endeavour is to install the induced rules as an automatic control programit is anticipated that this will lead to more consistent and reliable control performance.

The approach we study is based on the automatic induction of production rules from examples. The algorithms used are a product of the machine learning sub-field of artificial intelligence research.

We present experimental results describing induction of executable models of skilled human control behaviour. Experiments were performed on physical laboratory apparatus.

The paper describes a self-learning control system for a mobile robot. Based on sensor information the control system has to provide a steering signal in such a way that collisions are avoided. Since in our case no «examples« are available, the system learns on the basis of an external reinforcement signal which is negative in case of a collision and zero otherwise. We describe the adaptive algorithm which is used for a discrete coding of the state space, and the adaptive algorithm for learning the correct mapping from the input (state) vector to the output (steering) signal.

The development of decision support systems is motivated by socio-economic demand, and the opportunities offered by new hardware and software technologies. It goes without saying that these factors mutually stimulate each other as well. The purpose of this paper is to highlight some of the issues influencing the design of a prototype decision support application for a distributed object management project. Emphasis is put on features that are likely to gain widespread use since backed by mainstream companies. These are the role of deep knowledge processing tools, multimedia, and objectoriented approaches.

The cybernetic interface through which users can communicate with computers “as we may think” is the dream of human-computer interactions. Aiming at interfaces where machines adapt themselves to users' intention instead of users' adaptation to machines, we have been applying neural networks to realize electromyographic(EMG)-controlled prosthetic members—a historical heritage of the cybernetics. This paper proposes that EMG patterns can be analyzed and classified by neural networks. Through experiments and simulations, it is demonstrated that recognition of not only finger movement and torque but also joint angles in dynamic finger movement, based on EMG patterns, can be successfully accomplished.

The contribution of this paper is twofold. First, we focus on the application of a particular NARMAX (nonlinear ARMAX) model representation based on local models for adaptive decoupling. Second, in order to improve the robustness of the adaptive control algorithm we introduce a diagonal PI-controller in parallel with the adaptive decoupler. These controllers are separated in the frequency domain, such that the decoupler and PI-controller takes care of control actions at higher and lower frequencies, respectively. The parallel control structure supports incremental control design, in the sense that improved process knowledge is used to successively upgrade control performance. The concept is illustrated by a semi-realistic simulation example.

The paper concentrates on a Petri-net approach to fault diagnosis. The framework which is proposed allows the preconditions, logical links and mutual influences of the activities and states of functions and equipments to be naturally described, and a simulation-based fault isolation reasoning to be implemented. It is also a basis for a multi-model-based approach, since Petri nets and behavioral models can be easily connected. The framework is illustrated with the example of a pneumatic suspension system.

This paper deals with Failure Detection and Isolation procedures using the generalized Parity Space Approach. The decision scheme uses the structural properties of the residuals and leads to the analysis of fault events signatures. In a classical approach, these signatures are binary words. We propose a multivalued voting scheme, which allows to follow some specifications connected with false alarms and miss-isolation. The performances of the proposed approach are compared with these of the classical one using two examples.

Symptoms of the recent software crisis such as budget and schedule overruns or reliability and quality problemsforce the software industry to search for appropriate techniques to control and improve the software development process. A widely used approach to gain control over a software development environment is the quantification of the problems areas in terms of product, process and resource metrics. The ESPRIT II project 5494 AMI (Application of Metrics in Industry) aims to establish a new goal-oriented overall applicable approach that includes all advantages of existing strategies. It shall lead AMI to the status of a de facto European standard. The main outcome of AMI, the so-called “Metrics Users' Handbook” is targeting a wide range of people involved into software development and provides a detailed description of the approach covered by the four main AMI activities: Assess, Analyse, Metricate, Improve A rationale for AMI usage, description of the approach and experience gained during the validation are shown in this paper.