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

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

scholarly journals Closed-loop separation control using machine learning

2015 ◽  
Vol 770 ◽  
pp. 442-457 ◽  
Author(s):  
N. Gautier ◽  
J.-L. Aider ◽  
T. Duriez ◽  
B. R. Noack ◽  
M. Segond ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feedback Control ◽  
Genetic Programming ◽  
Closed Loop ◽  
Recirculation Zone ◽  
Low Frequency ◽  
Learning Control ◽  
Separation Control ◽  
Periodic Forcing ◽  
Model Free

We present the first closed-loop separation control experiment using a novel, model-free strategy based on genetic programming, which we call ‘machine learning control’. The goal is to reduce the recirculation zone of backward-facing step flow at $\mathit{Re}_{h}=1350$ manipulated by a slotted jet and optically sensed by online particle image velocimetry. The feedback control law is optimized with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimization is performed employing genetic programming. After 12 generations comprised of 500 individuals, the algorithm converges to a feedback law which reduces the recirculation zone by 80 %. This machine learning control is benchmarked against the best periodic forcing which excites Kelvin–Helmholtz vortices. The machine learning control yields a new actuation mechanism resonating with the low-frequency flapping mode instability. This feedback control performs similarly to periodic forcing at the design condition but outperforms periodic forcing when the Reynolds number is varied by a factor two. The current study indicates that machine learning control can effectively explore and optimize new feedback actuation mechanisms in numerous experimental applications.

Machine Learning Control For Highly Reconfigurable High-Order Systems

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

scholarly journals Machine Learning Control for Experimental Turbulent Flow Targeting the Reduction of a Recirculation Bubble

2017 ◽  
Author(s):  
Camila Chovet ◽  
Marc Lippert ◽  
Laurent Keirsbulck ◽  
Bernd R. Noack ◽  
Jean-Marc Foucaut
Keyword(s):  
Machine Learning ◽  
Turbulent Flow ◽  
Recirculation Zone ◽  
Learning Control ◽  
Control Logic ◽  
Periodic Forcing ◽  
Recirculation Bubble ◽  
Turbulence Control ◽  
Model Free ◽  
Free Machine

We experimentally control the turbulent flow over backward-facing step (ReH = 31500). The goal is to modify the internal (Xr) and external (Lr) recirculation points and consequently the recirculation zone (Ar). A model-free machine learning control (MLC) is used as control logic. As benchmark, an optimized periodic forcing is employed. MLC generalizes periodic forcing by a multi-frequency actuation. In addition, a sensor-based control and a non-autonomous feedback, open- and closed-loop laws, were use to optimize the control. The MLC multi-frequency forcing outperforms, as expected, periodic forcing. The non-autonomous feedback brings a further improvement. The unforced and actuated flows have been investigated in real-time with a TSI particle image velocimetry (PIV) system. The current study shows that a generalization of multi-frequency forcing and sensor feedback significantly reduces the turbulent recirculation zone, far beyond optimized periodic forcing. The study suggests that MLC can effectively explore and optimize new feedback actuation mechanisms and we anticipate MLC to be a game changer in turbulence control.

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