Title

Intelligent automated control of life support systems using proportional representations

Authors

Authors

A. S. Wu;Garibay, II

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

IEEE Trans. Syst. Man Cybern. Part B-Cybern.

Keywords

genetic algorithm (GA); life support system control; resource; allocation; proportional genetic algorithm; gene expression; proportional representation; stochastic hill-climbing (SH); ALLOCATION; Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics

Abstract

Effective automatic control of Advanced Life Support Systems (ALSS) is a crucial component of space exploration. An ALSS is a coupled dynamical system which can be extremely sensitive and difficult to predict. As a result, such systems can be difficult to control using deliberative and deterministic methods. We investigate the performance of two machine learning algorithms, a genetic algorithm (GA) and a stochastic hill-climber (SH), on the problem of learning how to control an ALSS, and compare the impact of two different types of problem representations on the performance of both algorithms. We perform experiments on three ALSS optimization problems using five strategies with multiple variations of a proportional representation for a total of 120 experiments. Results indicate that although a proportional representation can effectively boost GA performance, it does not necessarily have the same effect on other algorithms such as SH. Results also support previous conclusions [23] that multivector control strategies are an effective method for control of coupled dynamical systems.

Journal Title

Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics

Volume

34

Issue/Number

3

Publication Date

1-1-2004

Document Type

Article

Language

English

First Page

1423

Last Page

1434

WOS Identifier

WOS:000221578100010

ISSN

1083-4419

Share

COinS