Symbolic regression methods for control system synthesis

In this paper we use symbolic regression methods for control system synthesis. We compare three methods: network operator method, genetic programming and analytical programming. We developed variational versions of genetic programming and analytical programming to improve the search process efficiency. All the methods perform search over the set of the small variations of the given basic solution. Search efficiency depends on the basic solution. We give an example of control system synthesis for the unmanned vehicle with the state constraints over the set of the initial states using these methods. © 2014 IEEE.

Authors
Publisher
Institute of Electrical and Electronics Engineers Inc.
Language
English
Pages
587-592
Status
Published
Number
6961436
Year
2014
Organizations
  • 1 Dorodnicyn Computing Centre of RAS, Institution of Russian Academy of Sciences, Moscow, Russian Federation
  • 2 Cybernetics and Mechatronics Dept., Peoples' Friendship Univ. of Russia, Moscow, Russian Federation
Keywords
Control systems; Genetic algorithms; Genetic programming; Regression analysis; Basic solutions; Initial state; Network operator; Search efficiency; Search process; Small variations; State constraints; Symbolic regression; Control system synthesis
Date of creation
19.10.2018
Date of change
19.10.2018
Short link
https://repository.rudn.ru/en/records/article/record/5015/