Adaptive control with recurrent high-order neural networks : theory and industrial applications
著者
書誌事項
Adaptive control with recurrent high-order neural networks : theory and industrial applications
(Advances in industrial control)
Springer, 2000
大学図書館所蔵 全10件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references ( p. [185]-190 ) and index
内容説明・目次
内容説明
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ..., new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled.
George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.
目次
1. Introduction.- 1.1 General Overview.- 1.2 Book Goals & Outline.- 1.3 Notation.- 2. Identification of Dynamical Systems Using Recurrent High-order Neural Networks.- 2.1 The RHONN Model.- 2.1.1 Approximation Properties.- 2.2 Learning Algorithms.- 2.2.1 Filtered Regressor RHONN.- 2.2.2 Filtered Error RHONN.- 2.3 Robust Learning Algorithms.- 2.4 Simulation Results.- Summary.- 3. Indirect Adaptive Control.- 3.1 Identification.- 3.1.1 Robustness of the RHONN Identifier Owing to Unmodeled Dynamics.- 3.2 Indirect Control.- 3.2.1 Parametric Uncertainty.- 3.2.2 Parametric plus Dynamic Uncertainties.- 3.3 Test Case: Speed Control of DC Motors.- 3.3.1 The Algorithm.- 3.3.2 Simulation Results.- Summary.- 4. Direct Adaptive Control.- 4.1 Adaptive Regulation - Complete Matching.- 4.2 Robustness Analysis.- 4.2.1 Modeling Error Effects.- 4.2.2 Model Order Problems.- 4.2.3 Simulations.- 4.3 Modeling Errors with Unknown Coefficients.- 4.3.1 Complete Model Matching at |x| = 0.- 4.3.2 Simulation Results.- 4.4 Tracking Problems.- 4.4.1 Complete Matching Case.- 4.4.2 Modeling Error Effects.- 4.5 Extension to General Affine Systems.- 4.5.1 Adaptive Regulation.- 4.5.2 Disturbance Effects.- 4.5.3 Simulation Results.- Summary.- 5. Manufacturing Systems Scheduling.- 5.1 Problem Formulation.- 5.1.1 Continuous Control Input Definition.- 5.1.2 The Manufacturing Cell Dynamic Model.- 5.2 Continuous-time Control Law.- 5.2.1 The Ideal Case.- 5.2.2 The Modeling Error Case.- 5.3 Real-time Scheduling.- 5.3.1 Determining the Actual Discrete Dispatching Decision.- 5.3.2 Discretization Effects.- 5.4 Simulation Results.- Summary.- 6. Scheduling using RHONNs: A Test Case.- 6.1 Test Case Description.- 6.1.1 General Description.- 6.1.2 Production Planning & Layout in SHW.- 6.1.3 Problem Definition.- 6.1.4 Manufacturing Cell Topology.- 6.1.5 RHONN Model Derivation.- 6.1.6 Other Scheduling Policies.- 6.2 Results & Comparisons.- Summary.- References.
「Nielsen BookData」 より