Machine Learning Control for Mobile Robot by Approximation Extremals by Symbolic Regression

2021 ◽  
pp. 718-736
Author(s):  
Askhat Diveev
Keyword(s):  
Machine Learning ◽  
Mobile Robot ◽  
Learning Control ◽  
Symbolic Regression

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.

scholarly journals Synthesis of a mobile robot spatial stabilization system based on machine learning control by symbolic regression

2021 ◽  
Vol 22 (2) ◽  
pp. 129-138
Author(s):  
Askhat I. Diveev ◽  
Neder Jair Mendez Florez
Keyword(s):  
Machine Learning ◽  
Mobile Robot ◽  
Initial Conditions ◽  
Symbolic Regression ◽  
Synthesis Problem ◽  
Terminal Point ◽  
Control Synthesis ◽  
Stabilization System ◽  
Nonlinear Feedback ◽  
Regression Methods

The spatial stabilization system synthesis problem of the robot is considered. The historical overview of methods and approaches for solving the problem of control synthesis is given. It is shown that the control synthesis problem is the most important task in the field of control, for which there are no universal numerical methods for solving it. As one of the ways to solve this problem, it is proposed to use the method of machine learning based on the application of modern symbolic regression methods. This allows you to build universal algorithms for solving control synthesis problems. Several most promising symbolic regression methods are considered for application in control tasks. The formal statement of the control synthesis problem for its numerical solution is given. Examples of solving problems of synthesis of system of spatial stabilization of mobile robot by method of network operator and variation Cartesian genetic programming are given. The problem required finding one nonlinear feedback function to move the robot from thirty initial conditions to one terminal point. Mathematical records of the obtained control functions are given. Results of simulation of control systems obtained by symbolic regression methods are given.

EMG-driven Machine Learning Control of a Soft Glove for Grasping Assistance and Rehabilitation.

2022 ◽  
pp. 1-1
Author(s):  
Marek Sierotowicz ◽  
Nicola Lotti ◽  
Laura Nell ◽  
Francesco Missiroli ◽  
Ryan Alicea ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Control

Machine learning control — explainable and analyzable methods

2020 ◽  
Vol 412 ◽  
pp. 132582
Author(s):  
Markus Quade ◽  
Thomas Isele ◽  
Markus Abel
Keyword(s):  
Machine Learning ◽  
Learning Control

scholarly journals Ensemble modeling approach for rainfall/groundwater balancing

2007 ◽  
Vol 9 (2) ◽  
pp. 95-106 ◽  
Author(s):  
D. Laucelli ◽  
O. Giustolisi ◽  
V. Babovic ◽  
M. Keijzer
Keyword(s):  
Machine Learning ◽  
Genetic Programming ◽  
Empirical Evidence ◽  
Averaging Method ◽  
Total Error ◽  
Ensemble Methods ◽  
Real Data ◽  
Symbolic Regression ◽  
Ensemble Modeling ◽  
Physical Phenomena

This paper introduces an application of machine learning, on real data. It deals with Ensemble Modeling, a simple averaging method for obtaining more reliable approximations using symbolic regression. Considerations on the contribution of bias and variance to the total error, and ensemble methods to reduce errors due to variance, have been tackled together with a specific application of ensemble modeling to hydrological forecasts. This work provides empirical evidence that genetic programming can greatly benefit from this approach in forecasting and simulating physical phenomena. Further considerations have been taken into account, such as the influence of Genetic Programming parameter settings on the model's performance.

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