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proceedings of the 1998 iee colloquium on update on developments in intelligent control

Proceedings of the 1998 IEE Colloquium on Update on Developm...

引用

proceedings of the 1998 iee colloquium on update on developments in intelligent control

The proceedings contains 4 papers from the iee colloquium on update on developments in intelligent control. Topics discussed include: intelligent modeling, estimation and fusion;and artificial intelligence approaches ... 详细信息

关键词： intelligent control

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iee colloquium update on developments in intelligent control

IEE Colloquium Update on developments in intelligent control

引用

iee colloquium on update on developments in intelligent control (Ref. No. 1998/513)

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intelligent modelling, estimation and fusion

Intelligent modelling, estimation and fusion

引用

proceedings of the 1998 iee colloquium on update on developments in intelligent control

作者： Harris, C.J. Univ of Southampton Southampton United Kingdom

A precursor to control is modelling, for nonlinear, uncertain, timevarying, unknown dynamical processes, neurofuzzy algorithms(1) have many useful properties including, convergence in learning, real time adaptability, and transparency. Unfortunately, they suffer from the curse of dimensionality, and recent research(5) on parsimonious modelling schemas such as LOLIMOT, ASMOD, MASMOD, MENN, have shown how this problem can be effectively overcome. Equally, this method of databased modelling coupled with local operating point models(2) enables classical linear control and estimation algorithms to be applied directly to these processes. In this seminar the basic theory of intelligent modelling via Neurofuzzy Algorithms(1) will be developed, local models(2), local controllers(3,4), intelligent estimators(4,5) and applications in modelling, control and estimation (tracking) for advanced transportation(6,7) will be used to illustrate the basic principles. The talk will be supported by a series of practical demonstrations via videos.

关键词： intelligent control

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Artificial intelligence approaches to model-based control

Artificial intelligence approaches to model-based control

引用

proceedings of the 1998 iee colloquium on update on developments in intelligent control

作者： Irwin, George W. Queen's Univ of Belfast Belfast United Kingdom

A nonlinear modeling architecture, called the local model network (LMN), introduces transparency while offering distinct advantages for nonlinear model-based control. Simulation results for a pH neutralization process are used to illustrate the performance benefits of LMNs for nonlinear dynamic matrix control (DMC) and nonlinear internal model control (IMC).

关键词： control theory

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Artificial intelligence approaches to fault diagnosis

Artificial intelligence approaches to fault diagnosis

引用

proceedings of the 1998 iee colloquium on update on developments in intelligent control

作者： Patton, Ron J. Lopez-Toribio, C.J. Univ of Hull Hull United Kingdom

Fault diagnosis of control engineering systems can be based upon the generation of signals which reflect inconsistencies between the fault-free and faulty system operation - so-called residual signals. This paper outlines some recent approaches to the generation of residual signals using methods of integrating quantitative and qualitative system knowledge, based upon AI techniques.

关键词： intelligent control

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Recent developments in intelligent control

Recent developments in intelligent control

引用

proceedings of the 1998 iee colloquium on update on developments in intelligent control

作者： Warwick, Kevin Univ of Reading Reading United Kingdom

intelligent control, as a discipline, has certainly been one of main growth areas in the field of control systems over the last 5-10 years. Although the topic is relatively new in itself, a number of other research areas, some of them well established, have effectively been swallowed up under the overall intelligent control umbrella. This paper defines intelligent control and identifies the main sub-areas in which significant progress has been made and likely fruitful topics to pursue in the future. It is recognized that it is an almost impossible task to give due weight to all aspects of what is quite a well-researched field, something that is particularly true in a short paper. For a broader and/or in-depth view of the field readers are, therefore, directed to other texts as is the case for more in-depth views on certain topics.

关键词： intelligent control

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Recent developments in intelligent control

Recent developments in intelligent control

引用

iee colloquium on update on developments in intelligent control (Ref. No. 1998/513)

作者： K. Warwick Department of Cybernetics University of Reading Australia

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Artificial intelligence approaches to fault diagnosis

Artificial intelligence approaches to fault diagnosis

引用

iee colloquium on update on developments in intelligent control (Ref. No. 1998/513)

作者： R.J. Patton C.J. Lopez-Toribio Control & Intelligent Systems Engineering Research Group School of Engineering University of Hull Hull UK

Fault diagnosis of control engineering systems can be based upon the generation of signals which reflect inconsistencies between the fault-free and faulty system operation-so-called residual signals. This paper outlines some recent approaches to the generation of residual signals using methods of integrating quantitative and qualitative system knowledge, based upon AI techniques.

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intelligent modelling, estimation and fusion

Intelligent modelling, estimation and fusion

引用

iee colloquium on update on developments in intelligent control (Ref. No. 1998/513)

作者： C.J. Harris Department of Electronics & Computer Science University of Southampton UK

Summary form only given. A precursor to control is modelling. For nonlinear, uncertain, time-varying, unknown dynamical processes, neurofuzzy algorithms have many useful properties including convergence in learning, real-time adaptability, and transparency. Unfortunately, they suffer from the curse of dimensionality, and recent research on parsimonious modelling schemas such as LOLIMOT, ASMOD, MASMOD, MENN, have shown how this problem can be effectively overcome. Equally, this method of data-based modelling coupled with local operating point models enables classical linear control and estimation algorithms to be applied directly to these processes. In this seminar the basic theory of intelligent modelling via neurofuzzy algorithms will be developed, and local models, local controllers, intelligent estimators and applications in modelling, control and estimation (tracking) for advanced transportation will be used to illustrate the basic principles.

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Artificial intelligence approaches to model-based control

Artificial intelligence approaches to model-based control

引用

iee colloquium on update on developments in intelligent control (Ref. No. 1998/513)

作者： G.W. Irwin Advanced Control Engineering Centre Department of Electrical and Electronic Engineering Queen''s University Belfast Belfast UK

While of undoubted value for nonlinear identification and control of dynamic systems, neural networks have a number of limitations for practical applications. Thus, in online training, due consideration must be given to the necessity for regularisation with noisy data and to the choice of network architecture. More fundamentally, the nontransparent black-box nature of neural models make it difficult to include a priori system information, and to interpret the final structure meaningfully in terms of physical process characteristics. Neural approaches also fail to exploit the significant body of theoretical results available for conventional model-based control, making it difficult to analyse the closed-loop behaviour in terms of stability and robustness. The aim of this paper is to describe a nonlinear modelling architecture, called the local model network (LMN), which introduces transparency while offering distinct advantages for nonlinear model-based control. Simulation results for a pH neutralisation process are used to illustrate the performance benefits of LMNs for nonlinear dynamic matrix control (DMC) and for nonlinear internal model control (IMC).

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