Abstract
In this paper an initial approach to Intelligent Control (IC) using Genetic Programming (GP) for access to space applications is presented. GP can be employed successfully to design a controller even for complex systems, where classical controllers fail because of the high nonlinearity of the systems. The main property of GP, that is its ability to autonomously create explicit mathematical equations starting from a very poor knowledge of the considered plant, or just data, can be exploited for a vast range of applications. Here, GP has been used to design the control law in an Intelligent Control framework for a modified version of the Goddard Rocket problem in 3 different failure scenarios, where the approach to IC consists in an online re-evaluation of the control law using GP when a considerably big change in the environment or in the plant happens. The presented results are then used to highlight the potential benefits of the method, as well as aspects that will need further developments.
Original language | English |
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Title of host publication | 2020 International Joint Conference on Neural Networks (IJCNN) |
Place of Publication | Piscataway, NJ. |
Publisher | IEEE |
Number of pages | 8 |
ISBN (Print) | 9781728169279 |
DOIs | |
Publication status | Published - 28 Sept 2020 |
Event | IEEE World Congress on Computational Intelligence 2020 - Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020 https://wcci2020.org/ |
Conference
Conference | IEEE World Congress on Computational Intelligence 2020 |
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Abbreviated title | WCCI |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 19/07/20 → 24/07/20 |
Internet address |
Keywords
- genetic programming
- intelligent control
- machine learning
- evolutionary algorithm
- access to space