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Proceedings of the European Control Conference 1991, July 2-5, 1991, Grenoble, France
The scope of the symposium covers all major aspects of system identification, experimental modelling, signal processing and adaptive control, ranging from theoretical, methodological and scientific developments to a large variety of (engineering) application areas. It is the intention of the organizers to promote SYSID 2003 as a meeting place where scientists and engineers from several research communities can meet to discuss issues related to these areas. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring and fault detection, signal processing and communication, parameter estimation and inverse modelling, statistical analysis and uncertainty bounding, adaptive control and data-based controller tuning, learning, data mining and Bayesian approaches, sequential Monte Carlo methods, including particle filtering, applications in process control systems, motion control systems, robotics, aerospace systems, bioengineering and medical systems, physical measurement systems, automotive systems, econometrics, transportation and communication systems*Provides the latest research on System Identification*Contains contributions written by experts in the field*Part of the IFAC Proceedings Series which provides a comprehensive overview of the major topics in control engineering.
The type of control system used for electrical machines depends on the use (nature of the load, operating states, etc.) to which the machine will be put. The precise type of use determines the control laws which apply. Mechanics are also very important, because they affect performance. Another factor of essential importance in industrial applications is operating safety. Finally, the problem of how to control a number of different machines, whose interactions and outputs must be coordinated, is addressed and solutions are presented. These and other issues are addressed here by a range of expert contributors, each of whom are specialists in their particular field. This book is primarily aimed at those involved in complex systems design, but engineers in a range of related fields such as electrical engineering, instrumentation and control, and industrial engineering, will also find this a useful source of information.
Le travail présenté traite des problèmes de la modélisation et de l'identification des systèmes non linéaires. En faisant référence à un processus (système entraîné par un moteur pas à pas), nous avons élaboré, en s'appuyant sur les équations décrivant son fonctionnement, une représentation dans l'espace d'état permettant de définir ses grandeurs d'entrées, de sorties ainsi que de son état. Cette description nous conduisant à un modèle fortement non linéaire, nous déterminons, à partir d'hypothèses simplificatrices faites sur les non linéarités du système, un modèle linéarise. En s'appuyant sur cette nouvelle représentation, nous avons élaboré des méthodes d'identification, basées sur la réponse du processus pour une consigne d'entrée donnée. Elles consistent, à partir d'informations reçues sur le système, à choisir des points particuliers dont les caractéristiques conduisent de façon rapide et précise à la connaissance des paramètres du modèle linéarisé. Les algorithmes d'identification étant très sensibles aux bruits, nous déterminons une méthode de filtrage nécessaire à l'élimination de ces parasites après avoir présenté auparavant le dispositif nécessaire à l'obtention et à la mémorisation des informations recueillies sur le processus. Ensuite, nous sélectionnons, en fonction du filtre choisi, l'algorithme d'identification le mieux adapté à la détermination des paramètres du modèle décrivant l'actionneur.
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Written by two of Europe's leading robotics experts, this book provides the tools for a unified approach to the modelling of robotic manipulators, whatever their mechanical structure. No other publication covers the three fundamental issues of robotics: modelling, identification and control. It covers the development of various mathematical models required for the control and simulation of robots.·World class authority·Unique range of coverage not available in any other book·Provides a complete course on robotic control at an undergraduate and graduate level