Designing Machine Learning Control Law of Dynamic Bubble Burst Control Plate for Stall Suppression

2019 ◽  
Author(s):  
Shohei Asai ◽  
Homare Yamato ◽  
Yasuto Sunada ◽  
Kenichi Rinoie
Keyword(s):  
Machine Learning ◽  
Learning Control ◽  
Control Law ◽  
Control Plate ◽  
Bubble Burst

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

scholarly journals Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

2019 ◽  
Vol 292 ◽  
pp. 01010
Author(s):  
Mihailo Lazarević ◽  
Nikola Živković ◽  
Darko Radojević
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Closed Loop ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Control Law ◽  
Exact Linearization ◽  
Outer Loop ◽  
Control Scheme

The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.

Clean system inversion learning control law

Automatica ◽  
2005 ◽  
Vol 41 (9) ◽  
pp. 1549-1556 ◽  
Author(s):  
Yongqiang Ye ◽  
Danwei Wang
Keyword(s):  
Learning Control ◽  
Control Law ◽  
Clean System ◽  
System Inversion

The Satellite Attitude Control Law Design Based on Machine Learning

2014 ◽  
Vol 519-520 ◽  
pp. 741-746 ◽  
Author(s):  
Guo Jiang Sun ◽  
Jin Hui Li ◽  
Shi Ming Chen ◽  
Yun Feng Dong
Keyword(s):  
Machine Learning ◽  
Attitude Control ◽  
Fitness Function ◽  
Control Law ◽  
Sensors And Actuators ◽  
Control Laws ◽  
Satellite Attitude ◽  
Control Precision ◽  
Attitude Error ◽  
Satellite Attitude Control

Traditional optimization algorithms can only optimize parameters in control laws. Machine learning method can optimize parameters and evolve satellite attitude control law automatically under certain criterion. Single axis satellite attitude simulation system with noise was built up, which included satellite attitude dynamic model, sensors and actuators model. The control laws inputs were attitude error, attitude errors integral and angular velocity error, and outputs were actuators control instructions. Control laws fitness function was an attitude errors statistical function. With suitable function set selected for genetic programming (GP) and parse tree used to represent a control law expression, GP was used to evolve control law expression automatically. Simulation result shows that this method can evolve control law with uncertainties noise better. The evolved control law response and control precision are better than PID, and it can be used in satellite attitude 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
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