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Published on: March 2, 2015
Identification and decentralized adaptive control using dynamical neural networks with application to robotic
A Karakasoglu1, S I Sudharsanan, M K Sundareshan
1Dept. of Electr. and Comput. Eng., Arizona Univ., Tucson, AZ.
IEEE Transactions on Neural Networks
|January 1, 1993
Summary
This study introduces a fast neural network training method for online adaptive control of complex systems. It enables rapid control updates for tasks like robotic manipulator trajectory tracking.
Area of Science:
- Robotics
- Control Systems Engineering
- Artificial Intelligence
Background:
- Complex dynamical systems require rapid control updates for tasks like high-speed trajectory tracking.
- Nonlinear dynamics and parameter variations in systems like robotic manipulators pose significant control challenges.
Purpose of the Study:
- To develop an efficient neural network strategy for online adaptive control of complex dynamical systems.
- To enhance the speed of training schemes for learning system dynamics in real-time.
- To facilitate fast updating of control actions for applications such as robotic manipulators.
Main Methods:
- Proposed a multilayer neural network structure with dynamical nodes in the hidden layer.
- Employed a supervised learning scheme with a simple distributed updating rule.
- Utilized online identification and decentralized adaptive control for system management.
Main Results:
- Demonstrated a rapid convergence of the training scheme for learning system dynamics.
- Achieved satisfactory control of a multijointed robotic manipulator during high-speed trajectory tracking.
- Successfully implemented online identification and decentralized adaptive control.
Conclusions:
- The proposed neural network strategy enables efficient online adaptive control of complex, nonlinear systems.
- The fast training scheme is crucial for real-time control applications, particularly in robotics.
- The method offers a viable solution for managing systems with coupled dynamics and parameter variations.
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