Automatic Linear Robot Control Synthesis Using Genetic Programming

2014 ◽  
pp. 601-618 ◽  
Author(s):  
Tiberiu S. Letia ◽  
Octavian Cuibus
Keyword(s):  
Genetic Programming ◽  
Robot Control ◽  
Control Synthesis

scholarly journals Control Synthesis as Machine Learning Control by Symbolic Regression Methods

2021 ◽  
Vol 11 (12) ◽  
pp. 5468
Author(s):  
Elizaveta Shmalko ◽  
Askhat Diveev
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Mathematical Formulation ◽  
Small Variation ◽  
Learning Control ◽  
Symbolic Regression ◽  
Control Technique ◽  
Control Synthesis ◽  
Basic Solution ◽  
Regression Methods

The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot.

Reactive and Memory-Based Genetic Programming for Robot Control

1999 ◽  
pp. 161-172 ◽  
Author(s):  
Björn Andersson ◽  
Per Svensson ◽  
Peter Nordin ◽  
Mats Nordahl
Keyword(s):  
Genetic Programming ◽  
Robot Control

Robot Control Synthesis in Conjunction with Moving Workpieces

RoManSy 6 ◽  
1987 ◽  
pp. 342-349
Author(s):  
P. Kiriazov ◽  
P. Marinov
Keyword(s):  
Robot Control ◽  
Control Synthesis
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