Intelligent automated control of life support systems using proportional representations

    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