Intelligent automated control of advanced life support systems

Project goals

Effective automatic control of Advance Life Support Systems (ALSS) which manage the resources of planetary and space habitats is a crucial component of space exploration. An ALSS is a coupled dynamical system which can be extremely sensitive and difficult to predict. We are interested in studying the use of machine learning methods such as the genetic algorithm (GA) on the problem of learning how to control an ALSS. We have implemented an ALSS simulator that models the basic processes occurring in the Bioregenerative Planetary Life Support System Test Complex (BIO-Plex) developed by NASA Johnson Space Center. We use this simulation to explore the effectiveness of GA approaches to learning effective ALSS control strategies for multiple optimization problems.


Our studies focus on the effectiveness of the proportional GA (PGA) applied to ALSS control.


Related links

[EC Lab] [Computer Science] [School of EECS] [UCF]

Last Updated: 8/22/2003