There has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an "on-and-off" fasion. This text is dedicated to issues on adaptive control of robots based on neural networks. The text has been tailored to give a comprehensive study of robot dynamics, present structured network models for robots, and provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint robots, and robots in constraint motion. Rigorous proof of the stability properties of adaptive neural network controllers is provided. Simulation examples are also presented to verify the effectiveness of the controllers, and practical implementation issues associated with the controllers are also discussed.
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dynamic modelling,
By Le Hoai Quoc (hochiminh city, Vietnam) - See all my reviews
This review is from: Adaptive Neural Network Control of Robotic Manipulators (World Scientific Series in Robotics and Intelligent Systems) (Hardcover)
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Inside This Book
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First Sentence:
Robotic manipulators are highly non-linear, dynamically coupled multi-axis electro-mechanical systems that are commonly used in tasks such as paint spraying, welding and material handling in manufacturing, and as accurate positioning systems, e.g. anti-aircraft guns, missile platforms, subsea operation machines, etc. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
double xdot, robust control term, adaptive neural network controllers, mode control term, velocity tracking performances, controller singularity, task space dynamics, rigid body robots, constrained motion control, flexible joint robots, task space control, network approximation errors, position tracking performance, based adaptive controller, static neural networks, linkage manipulator, dynamic neural network model, singular perturbation model, multiple manipulators, network emulator, computed torque control, parameter adaptation laws, boundary layer system, single manipulator, target impedance Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Parametric Network Model, Variables Declaration, Joint Angle, Function Declaration, American Control Conf, Dash Line, Englewood Cliffs, Industrial Electronics, Solid Line, Systems Science, Canudas de Wit, Input Layer Second Layer Output Layer, Substituting Eqs, Technical Report, Tychonov's Theorem, Academic Press, Gaussian Weight, Main File, Mechanical Engineers, Prentice Hall, San Francisco, University of Illinois New!
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Robotic manipulators are highly non-linear, dynamically coupled multi-axis electro-mechanical systems that are commonly used in tasks such as paint spraying, welding and material handling in manufacturing, and as accurate positioning systems, e.g. anti-aircraft guns, missile platforms, subsea operation machines, etc. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
double xdot, robust control term, adaptive neural network controllers, mode control term, velocity tracking performances, controller singularity, task space dynamics, rigid body robots, constrained motion control, flexible joint robots, task space control, network approximation errors, position tracking performance, based adaptive controller, static neural networks, linkage manipulator, dynamic neural network model, singular perturbation model, multiple manipulators, network emulator, computed torque control, parameter adaptation laws, boundary layer system, single manipulator, target impedance Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, Parametric Network Model, Variables Declaration, Joint Angle, Function Declaration, American Control Conf, Dash Line, Englewood Cliffs, Industrial Electronics, Solid Line, Systems Science, Canudas de Wit, Input Layer Second Layer Output Layer, Substituting Eqs, Technical Report, Tychonov's Theorem, Academic Press, Gaussian Weight, Main File, Mechanical Engineers, Prentice Hall, San Francisco, University of Illinois New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 46
books:
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14
books
cite this book:
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- Adaptive Control (An Independent Learning Module from the Instrument Society of America) by Chang C. Hang in Front Matter, page 331, and page 335
- Neural Networks Hb (UNICOM - Information & Communications Technology) by J G Taylor on page 337, and page 338
- Adaptive Control (2nd Edition) by Karl Johan Astrom on page 327, and page 330
- Adaptive Control by B. D. O. Anderson on page 327
- Adaptive Control (Communications and Control Engineering) by Rogelio Lozano on page 335
See all 46 books this book cites
- Bio-Inspired Applications of Connectionism: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, ... II (Lecture Notes in Computer Science) (Pt.2) by Jose Mira on page 54
- Biomimicry for Optimization, Control, and Automation by Kevin M. Passino in Back Matter
- Robust and H-infinity Control (Communications and Control Engineering) by Ben M. Chen in Back Matter
- Automatic Systems for Building the Infrastructure in Developing Countries 2003: Knowledge and Technology Transfer (IPV - IFAC Proceedings Volume) by Y. Istefanopulos on page 17
- Fuzzy Decision Making in Modeling and Control (World Scientific Series in Robotics and Intelligent Systems) by Joao M. C. Sousa in Front Matter
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