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Related Experiment Video

Updated: Jul 7, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Distributed intelligent control system for a continuous-state plant.

A Stothert¹, I M Macleod

¹Dept. of Electr. Eng., Univ. of the Witwatersrand, Johannesburg.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|January 1, 1997

Summary

This summary is machine-generated.

This study explores distributed artificial intelligence for continuous-state control systems. It introduces a new knowledge distinction to aid designing intelligent controllers for complex plants like mine winders.

Related Experiment Videos

Last Updated: Jul 7, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Area of Science:

Control Engineering
Artificial Intelligence
Distributed Systems

Background:

Continuous-state plants present unique challenges for intelligent control systems.
Existing multi-agent systems require specific structures for effective operation.
Distributed artificial intelligence offers potential solutions for complex control problems.

Purpose of the Study:

To investigate the application of distributed artificial intelligence (DAI) for continuous-state control.
To discuss agent selection and interaction strategies in DAI controllers.
To introduce a novel knowledge distinction for simplifying controller design.

Main Methods:

Investigated DAI techniques for continuous-state control.
Analyzed agent interaction and selection within a distributed framework.
Introduced a priori and operational knowledge concepts.
Conducted a simulation study using a deep-shaft mine winder model.

Main Results:

Demonstrated the feasibility of DAI for continuous-state plant control.
The proposed knowledge distinction aids in designing distributed intelligent controllers.
Simulation results validate the controller's effectiveness on a complex industrial system.

Conclusions:

Distributed artificial intelligence is a viable approach for continuous-state control.
The a priori and operational knowledge framework simplifies the design of DAI controllers.
The deep-shaft mine winder application highlights the practical utility of this approach.