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Adaptive Neural Networks and Robot Intelligent Control in Direct or Indirect Interaction with Humans

  • ID: 3947138
  • Book
  • 120 Pages
  • Elsevier Science and Technology
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Adaptive Neural Networks and Robot Intelligent Control in Direct or Indirect Interaction with Humans offers a particular methodology for using neural networks to solve control problems of nonlinear systems interacting directly (mobile robot exoskeleton type) or indirectly with humans (redundant robot manipulators serial or parallel). In addition, the book provides novel perspectives and research ideas for further strengthening the presence of humans in the control loop (intention, thought, etc.). The robots used for illustration purposes were designed in collaboration with industry.

  • Offers a methodology for using neural networks to solve control problems of nonlinear systems interacting directly or indirectly with humans
  • Provides novel perspectives and research ideas for further strengthening the presence of humans in the control loop
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1. Neural Network Layers 2. Robot Manipulators and Redundancy 3. Portable Robots and Support 4. Adaptive Neural Control of Redundant Robots with Obstacle Avoidance 5. Control Force / Position of Parallel Robots 6. Control Mobile Robots 7. Towards Neuronal Control

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Daachi, Boubaker
Boubaker Daachi is Full Professor at University of Paris-8 in France and research member at Computer-Science Laboratory LIASD-EA4383. His research interests include Neural Networks, control, localization, routing protocols and security with applications in wireless sensor networks, Brain Computer Interfaces and robotics.
Madani, Tarek
Tarek Madani is a Professor at Universite Paris-Est Creteil Val de Marne with expertise in Electronic Engineering, Electrical Engineering, Control Systems Engineering
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