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Updated: Jul 7, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Robust neural-network control of rigid-link electrically driven robots.

C Kwan¹, F L Lewis, D M Dawson

¹Intelligent Automation Incorporated, Rockville, MD 20850, USA.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This study introduces a robust neural-network (NN) controller for rigid-link electrically driven (RLED) robots. The controller offers online tuning and guarantees stability without complex preliminary analysis, making it universally applicable.

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Robotics
Control Systems Engineering
Artificial Intelligence

Background:

Rigid-link electrically driven (RLED) robots require sophisticated control for precise motion.
Existing adaptive robot controllers often necessitate extensive preliminary analysis and regression matrix determination.
Neural networks (NNs) offer potential for approximating complex nonlinear functions in robotic systems.

Purpose of the Study:

To propose a robust neural-network (NN) controller for the motion control of RLED robots.
To develop an NN controller that avoids lengthy off-line learning phases.
To ensure guaranteed stability of tracking errors and NN weights.

Main Methods:

Utilized two-layer neural networks (NNs) to approximate complex nonlinear functions inherent in RLED robot dynamics.
Implemented an online tuning mechanism for NN weights, eliminating the need for offline training.
Developed a control strategy that guarantees uniformly ultimately bounded (UUB) stability for both tracking errors and NN weights.

Main Results:

The proposed NN controller successfully controls the motion of RLED robots.
Online tuning of NN weights was achieved without requiring an offline learning phase.
Uniformly ultimately bounded (UUB) stability of tracking errors and NN weights was mathematically guaranteed.
The controller eliminated the need for determining a regression matrix, simplifying the control design process.

Conclusions:

The developed NN controller provides a robust and stable solution for RLED robot motion control.
The controller's universal applicability to any RLED robot without modification represents a significant advancement.
This approach simplifies adaptive control design by removing the requirement for extensive preliminary analysis.