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Summary

This summary is machine-generated.

This study introduces a novel broad learning system (BLS) for robot motion control, simplifying design without complex modeling. The BLS effectively guides magnetic robots to targets while avoiding obstacles.

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Area of Science:

Robotics
Control Theory
Machine Learning

Background:

Learning-based methods simplify robot controller design and parameter tuning.
Traditional methods often require detailed dynamic system modeling.

Purpose of the Study:

To develop a robot motion control policy using a broad learning system (BLS).
To apply the BLS controller to a small-scale magnetic robotic system for point-reaching tasks.
To demonstrate effective control without detailed mathematical modeling.

Main Methods:

A broad learning system (BLS) was employed for robot motion control.
Lyapunov theory was used to derive parameter constraints for BLS controller nodes.
A small-scale magnetic fish robot was designed and trained using the BLS controller.

Main Results:

The BLS-based controller successfully guided the magnetic fish robot's motion.
The robot converged to the targeted area along the BLS trajectory.
Obstacle avoidance was successfully demonstrated.

Conclusions:

The proposed BLS-based control method is effective for robot motion control, particularly in systems without detailed dynamic models.
This approach simplifies controller design and training for robotic applications.
The method shows promise for real-world applications like magnetic microrobots.