An immuno control framework for decentralized mechatronic control

Albert Ko*, H. Y.K. Lau, T. L. Lau

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

18 Citations (Scopus)

Abstract

The Immune System is a complex adaptive system containing many details and many exceptions to established rules. Exceptions such as the suppression effect that causes T-cells to develop reversible aggressive and tolerant behaviors create difficulties for the study of immunology but also give hints to how artificial immune systems may be designed. Presented in this paper is the General Suppression Framework, which models the suppression hypothesis of the immune discrimination theory. A distributed control system based on the proposed framework is designed to control a modular robot configured into a planar manipulator arm. The modules can generate emergent group behaviors by exhibiting aggressive or tolerant behavior based on the environment change. A MATLAB simulation program is developed to demonstrate the effectiveness of the suppression mechanism and a mechanical arm is constructed to verify the control actions of the mechanism. The ultimate ambition of this work is to understand how the suppression mechanism affects the discrimination system and in turn affect other integral parts of the artificial immune system.

Original languageEnglish
Title of host publicationICARIS 2004 : Artificial Immune Systems - Third International Conference Proceedings
Pages91-105
Number of pages15
Volume3239
DOIs
Publication statusPublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
ISSN (Print)0302-9743

Fingerprint

Mechatronics
Immune system
Decentralized
Artificial Immune System
Exception
Discrimination
Modular robots
Immunology
Distributed Control System
Matlab Simulation
Distributed parameter control systems
T-cells
Adaptive systems
Immune System
Adaptive Systems
Manipulator
MATLAB
Manipulators
Complex Systems
Robot

Keywords

  • Artificial Immune Systems
  • Distributed Control
  • Multi-agents

Cite this

Ko, A., Lau, H. Y. K., & Lau, T. L. (2004). An immuno control framework for decentralized mechatronic control. In ICARIS 2004 : Artificial Immune Systems - Third International Conference Proceedings (Vol. 3239, pp. 91-105). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-30220-9_8
Ko, Albert ; Lau, H. Y.K. ; Lau, T. L. / An immuno control framework for decentralized mechatronic control. ICARIS 2004 : Artificial Immune Systems - Third International Conference Proceedings. Vol. 3239 2004. pp. 91-105 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Ko, A, Lau, HYK & Lau, TL 2004, An immuno control framework for decentralized mechatronic control. in ICARIS 2004 : Artificial Immune Systems - Third International Conference Proceedings. vol. 3239, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 91-105. https://doi.org/10.1007/978-3-540-30220-9_8

An immuno control framework for decentralized mechatronic control. / Ko, Albert; Lau, H. Y.K.; Lau, T. L.

ICARIS 2004 : Artificial Immune Systems - Third International Conference Proceedings. Vol. 3239 2004. p. 91-105 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

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Ko A, Lau HYK, Lau TL. An immuno control framework for decentralized mechatronic control. In ICARIS 2004 : Artificial Immune Systems - Third International Conference Proceedings. Vol. 3239. 2004. p. 91-105. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-30220-9_8