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In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

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Neural-network control of mobile manipulators.

S Lin¹, A A Goldenberg

¹Mechanical and Industrial Engineering Department, University of Toronto, Toronto, ON M5S 3G8, Canada. slin@mie.utoronto.ca

IEEE Transactions on Neural Networks

|February 6, 2008

Summary

A novel neural network (NN) controller enhances mobile manipulator motion control by learning unknown dynamics online. This method guarantees stability and outperforms conventional robust control, even with disturbances.

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Area of Science:

Robotics
Control Systems Engineering
Artificial Intelligence

Background:

Mobile manipulators require precise motion control, especially with unknown dynamics and kinematic constraints.
Existing control methods may struggle with unmodeled dynamics and external disturbances.

Purpose of the Study:

To develop a neural network (NN)-based control methodology for mobile manipulators.
To address motion control challenges in the presence of unknown dynamics and kinematic constraints.
To ensure robust performance and stability under unmodeled disturbances.

Main Methods:

Online identification of unknown manipulator dynamics using NN estimators.
Development of a disturbance-rejection controller with guaranteed stability.
No preliminary NN weight training required.

Main Results:

The proposed NN controller demonstrated superior performance compared to conventional robust control.
Experimental validation on a 4-DOF manipulator arm confirmed the controller's effectiveness.
Guaranteed tracking stability, NN weight convergence, and bounded estimation errors.

Conclusions:

The NN-based methodology offers a robust and effective solution for mobile manipulator motion control.
The controller's ability to handle unknown dynamics and disturbances is a significant advancement.
This approach provides a promising alternative to traditional control strategies.