Adaptive Neural Network Control For Robotic Manipulators A Practical Guide To Smarter Motion And Precise Positioning So Your Industrial Automation Runs Slick With AI Techniques
UPC:
✔️ Single book format with in-depth, practical adaptive control theory
This book explains adaptive neural network control for robotic manipulators, ideal for engineers and researchers working in industrial automation and AI assisted motion systems. It covers robot dynamics, structured network models, and adaptive controller design for rigid robots, flexible joints, and robots in constraint motion, with stability proofs and simulation examples.
✅ Pages: 396
✅ Language: English
✅ Author: Ge S S
✅ Focus: adaptive neural network based control for robotic manipulators and practical implementation
✅ Includes: stability proofs and simulation validation
💡 What is adaptive neural network control used for in robotic manipulators?
Adaptive neural network control is handy for managing changing dynamics and achieving precise positioning in automation.
- Addresses robot dynamics with learning and adaptation
- Handy for rigid robots, flexible joints, and constrained motion
- Includes stability proofs and simulation validation
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