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Dynamic Neural Networks for Motion Control of Redundant Manipulators / by Mei Liu, Jingkun Yan, Renpeng Huang.
Springer eBooks EBA - Intelligent Technologies and Robotics Collection 2026 Available online
View online- Format:
- Book
- Author/Creator:
- Liu, Mei.
- Series:
- Intelligent Control and Learning Systems, 2662-5466 ; 21
- Language:
- English
- Subjects (All):
- Automatic control.
- System theory.
- Control theory.
- Robotics.
- Automation.
- Control and Systems Theory.
- Systems Theory, Control.
- Control, Robotics, Automation.
- Local Subjects:
- Control and Systems Theory.
- Systems Theory, Control.
- Control, Robotics, Automation.
- Physical Description:
- 1 online resource (303 pages)
- Edition:
- 1st ed. 2026.
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2026.
- Summary:
- This book discusses the development and application of dynamic neural networks (DNNs) for solving complex motion control problems in redundant manipulators. Specifically, it presents a series of advanced DNNs, including noise-rejection DNNs, fuzzy-parameter DNNs, and so on, which are designed to optimize performance while ensuring robustness and computational efficiency. Based on the presented DNNs, this book further constructs a series of motion control schemes for redundant manipulators to address some key challenges such as cyclic motion, position and orientation tracking, and model-unknown scenarios. Each method is rigorously demonstrated for the convergence, and its effectiveness is validated through simulations and physical experiments. By integrating computational intelligence with control theory, this book provides a comprehensive framework for solving time-varying and noise-perturbed problems in robotics, making it a valuable resource for researchers and practitioners in the field.
- Contents:
- 1. Double-Index Control With DNN
- 2. Cyclic Motion Control With Noise-Rejection DNN
- 3. Trajectory-Tracking MPC With Z-type DNN
- 4. Motion/Force Control With Fuzzy DNN
- 5. Orientation Tracking Incorporated Multi-Criteria Control With DNN
- 6. Position and Orientation-Tracking MPC With Finite-Time DNN
- 7. Data-Driven RC2M Control With DNN
- 8. Cerebellum-Inspired MPC With Discrete DNN.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 981-9691-44-3
- 9789819691449
- OCLC:
- 1570553235
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