Machine Learning Control For Highly Reconfigurable High-Order Systems

2015 ◽  
Author(s):  
John Valasek ◽  
Suman Chakravorty
Keyword(s):  
Machine Learning ◽  
Learning Control ◽  
High Order

Airfoil Stall Suppression Effects by Use of Dynamic Bubble Burst Control Plate Utilizing Machine Learning Control

2020 ◽  
Vol 68 (5) ◽  
pp. 195-203
Author(s):  
Shohei Asai ◽  
Kento Kato ◽  
Homare Yamato ◽  
Yasuto Sunada ◽  
Kenichi Rinoie
Keyword(s):  
Machine Learning ◽  
Learning Control ◽  
Control Plate ◽  
Suppression Effects ◽  
Bubble Burst

Optimization and sensitivity analysis of active drag reduction of a square-back Ahmed body using machine learning control

2020 ◽  
Vol 32 (12) ◽  
pp. 125117
Author(s):  
Dewei Fan ◽  
Bingfu Zhang ◽  
Yu Zhou ◽  
Bernd R. Noack
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Drag Reduction ◽  
Learning Control ◽  
Active Drag ◽  
Ahmed Body

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.

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
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