Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach

2014 ◽  
Vol 23 ◽  
pp. 444-451 ◽  
Author(s):  
Mathew Mithra Noel ◽  
B. Jaganatha Pandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Liquid Level ◽  
Learning Approach ◽  
Level System ◽  
Artificial Neural

scholarly journals Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control

2019 ◽  
Vol 865 ◽  
pp. 281-302 ◽  
Author(s):  
Jean Rabault ◽  
Miroslav Kuchta ◽  
Atle Jensen ◽  
Ulysse Réglade ◽  
Nicolas Cerardi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Flow Control ◽  
Mass Flow ◽  
Active Flow Control ◽  
Flow Rates ◽  
Mass Flow Rates ◽  
Artificial Neural ◽  
Active Flow

We present the first application of an artificial neural network trained through a deep reinforcement learning agent to perform active flow control. It is shown that, in a two-dimensional simulation of the Kármán vortex street at moderate Reynolds number ($Re=100$), our artificial neural network is able to learn an active control strategy from experimenting with the mass flow rates of two jets on the sides of a cylinder. By interacting with the unsteady wake, the artificial neural network successfully stabilizes the vortex alley and reduces drag by approximately 8 %. This is performed while using small mass flow rates for the actuation, of the order of 0.5 % of the mass flow rate intersecting the cylinder cross-section once a new pseudo-periodic shedding regime is found. This opens the way to a new class of methods for performing active flow control.

scholarly journals Optimal Energy Routing Design in Energy Internet with Multiple Energy Routing Centers Using Artificial Neural Network-Based Reinforcement Learning Method

2019 ◽  
Vol 9 (3) ◽  
pp. 520 ◽  
Author(s):  
Dan-Lu Wang ◽  
Qiu-Ye Sun ◽  
Yu-Yang Li ◽  
Xin-Rui Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforcement Learning ◽  
Energy Structure ◽  
Energy Internet ◽  
Optimal Energy ◽  
Information Layer ◽  
Routing Design ◽  
Artificial Neural ◽  
Energy Routing

In order to cope with the energy crisis, the concept of an energy internet (EI) has been proposed as a novel energy structure with high efficiency which allows full play to the advantages of multi-energy coupling. In order to adapt to the multi-energy coupled energy structure and achieve flexible conversion and interaction of multi-energy, the concept of energy routing centers (ERCs) is proposed. A two-layered structure of an ERC is established. Multi-energy conversion devices and connection ports with monitoring functions are integrated in the physical layer which allows multi-energy flow with high flexibility. As for the EI with several ERCs connected to each other, energy flows among them are managed by an energy routing controller located in the information layer. In order to improve the efficiency and reduce the operating cost and environmental cost of the proposed EI, an optimal multi-energy management-based energy routing design problem is researched. Specifically, the voltages of the ERC ports are managed to regulate the power flow on the connection lines and are restricted on account of security operations. An artificial neural network (ANN)-based reinforcement learning algorithm was proposed to manage the optimal energy routing path. Simulations were done to verify the effectiveness of the proposed method.

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