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Download Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities (Frontiers in Applied Mathematics) ePub

by F. L. Lewis,J. Campos,R. Selmic

Download Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities (Frontiers in Applied Mathematics) ePub
  • ISBN 0898715059
  • ISBN13 978-0898715057
  • Language English
  • Author F. L. Lewis,J. Campos,R. Selmic
  • Publisher Society for Industrial and Applied Mathematics (January 1, 1987)
  • Pages 258
  • Formats lrf rtf txt azw
  • Category Engineering
  • Subcategory Engineering
  • Size ePub 1490 kb
  • Size Fb2 1287 kb
  • Rating: 4.8
  • Votes: 710

Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics. Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities such as time delay, friction, deadzone, and backlash that can be found in all industrial motion systems, plus a thorough development, rigorous stability proofs, and simulation examples for each design. In the final chapter, the authors develop a framework to implement intelligent control schemes on actual systems. Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications.

L. Lewis, J. Campos, R. Selmic. Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics.

L.

Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics.

Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics

Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics.

Lewis, Javier Campos, Rastko Selmic. This book brings neural networks and fuzzy logic together with dynamical control systems.

1 Two-Layer Neural Network Topology and Tuning. Both NN and FL systems belong to a larger class of systems called nonlinear network structures that have some properties of extreme importance for feedback control systems. These are made up of multiple interconnected nodes, and can learn by modifying the weights interconnecting the nodes.

Cardiovascular and Respiratory Systems: Modeling, Analysis, and Control (Frontiers in Applied Mathematics). Actuator Saturation Control.

Several powerful modern control approaches are used in this book for the design of intelligent controllers.

Neural Network-Based Compensation of Synthetic Jet Actuator Nonlinearities for Aircraft Flight Control. Several powerful modern control approaches are used in this book for the design of intelligent controllers.

Robot Manipulator Control, 2nd ed. Marcel Dekker, New York (2004)Google Scholar. Beyond regression: new tools for prediction and analysis in the behavior sciences, P. Thesis, Committee on Appl. Harvard University (1974)Google Scholar.

F. L. Published: 1 January 2002.

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