Neural Network Model-Based Control for Manipulator: An Autoencoder Perspective.

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Neural network model-based control is gaining traction for manipulator kinematics.
• Autoencoders enhance deep learning but struggle with noisy data in manipulator control.
• Integrating model knowledge into autoencoder controllers for robustness is a key challenge.

Purpose of the Study:

• To propose a novel sparse autoencoder controller for manipulator kinematic control.
• To enhance controller robustness and reliability by incorporating robot model knowledge.
• To address input saturation in controller design for practical systems.

Main Methods:

• Developed a sparse autoencoder controller using robot model weights, bypassing traditional training data.
• Employed a dynamic recurrent neural network for encoding and decoding control targets.
• Incorporated input saturation into the controller construction and utilized sparse optimization.

Main Results:

• The proposed controller enables efficient end-effector path tracking for manipulator systems.
• Demonstrated effective performance even with additive noise and parameter uncertainty.
• Validated the controller's robustness and reliability through theoretical analysis and simulations.

Conclusions:

• The novel sparse autoencoder controller offers a robust and reliable solution for manipulator kinematic control.
• Integrating model knowledge and considering input saturation are crucial for practical autoencoder-based control.
• The approach shows significant potential for improving manipulator performance in real-world applications.