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Neural Flocking: MPC-based Supervised Learning of Flocking Controllers
[article]
2020
arXiv
pre-print
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By learning a symmetric distributed neural flocking controller from a centralized MPC-based flocking controller, we achieve the best of both worlds: the neural controllers have high performance (on par ...
Our approach is based on supervised learning, with the centralized controller providing the training data to the learning agent, i.e., the synthesized distributed controller. ...
In this paper, we present Neural Flocking (NF), a new approach to the flocking problem that uses Supervised Learning to learn a symmetric and fully distributed flocking controller from a centralized MPC-based ...
arXiv:1908.09813v2
fatcat:rbz3y7vdo5hjdatwjia65majzm
Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers
[chapter]
2020
Lecture Notes in Computer Science
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By learning a symmetric and distributed neural flocking controller from a centralized MPC-based one, we achieve the best of both worlds: the neural controllers have high performance (on par with the MPC ...
Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. ...
Flocking: MPC-based Supervised Learning of Flocking Controllers ...
doi:10.1007/978-3-030-45231-5_1
fatcat:blmtsikgc5d2xilfwicx4dym5e
Machine Learning Methods for Management UAV Flocks - a Survey
2021
IEEE Access
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For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. ...
Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. ...
Flocking control can be performed by the learning-based model predictive control (LBMPC), developed by Aswani et al. [10] . ...
doi:10.1109/access.2021.3117451
fatcat:f6xli6srencw3ezqg5fyzwmuie
Iterative Learning for Model Reactive Control: Application to Autonomous Multi-agent Control
2021
2021 7th International Conference on Automation, Robotics and Applications (ICARA)
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In this paper, a decentralized autonomous controller aimed to control a fleet of quadrotors is designed, based on the iterative generation and exploitation of logged traces. ...
In the exploitation phase, a policy is learned from the traces generated in the previous phase, and the policy is iteratively refined, to achieve a robust reactive control of each quadrotor agent. ...
and the GNN is replaced by supervised learning neural network. ...
doi:10.1109/icara51699.2021.9376454
fatcat:kej7zblnunelbc4wiggigdk5vy
Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control
2020
Energies
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This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. ...
The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under ...
neural network-based model predictive control (ANN-MPC). ...
doi:10.3390/en13174339
fatcat:dwejhyvxgfbhpk2zoqbvtvevh4
Learning Distributed Controllers for V-Formation
[article]
2020
arXiv
pre-print
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over the MPC-based controller. ...
We show how a high-performing, fully distributed and symmetric neural V-formation controller can be synthesized from a Centralized MPC (Model Predictive Control) controller using Deep Learning. ...
We employ Supervised Learning to train our neural controller with the trajectories obtained from CAMPC. ...
arXiv:2006.00680v1
fatcat:cwjdumrjojeqthni6iocxlvs64
Formation Control for a Fleet of Autonomous Ground Vehicles: A Survey
2018
Robotics
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At present, most of the developments target standalone autonomous vehicles, which can sense the surroundings and control the vehicle based on this perception, with limited or no driver intervention. ...
In other words, many distributed and decentralized approaches of vehicle formation control are studied and their implementations are discussed. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/robotics7040067
fatcat:wo7eomrs75dmleum5o6tvltgmu
Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies
2019
Renewable & Sustainable Energy Reviews
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Achieving an energy-efficient powertrain requires tackling several conflicting control objectives such as the drivability, fuel economy, reduced emissions, and battery state of charge preservation, which ...
A R T I C L E I N F O Keywords: Plug-in hybrid electric vehicle Full-electric vehicle Energy management strategy optimisation Online EMS Offline EMS Optimal control strategy A B S T R A C T Hybrid and ...
We also acknowledge Flanders Make and VLAIO for the support of our research group. ...
doi:10.1016/j.rser.2019.109596
fatcat:ybks774km5htvb6f7foyetum7m
Recent Advances in Formations of Multiple Robots
2021
Current Robotics Reports
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new architectures and tools (e.g., ML-based formation control). ...
For instance, consensus-based control and collision avoidance are usually intertwined together for the sake of reaching a consensus in a manner which is collision-free. ...
The three basic machine learning paradigms of supervised learning, unsupervised learning and reinforcement learning are widely utilized for the sake of formation creation and maintaining it. ...
doi:10.1007/s43154-021-00049-2
fatcat:xu4xzhaqpvduljk6pvf34z6fva
Virtual Inertia Control Methods in Islanded Microgrids
2021
Energies
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Some of the reviewed methods are the coefficient diagram method, H-infinity-based methods, reinforcement-learning-based methods, practical-swarm-based methods, fuzzy-logic-based methods, and model-predictive ...
Among the numerous studies on frequency stability, one key approach is based on integrating an additional loop with virtual inertia control, designed to mimic the behavior of traditional synchronous machines ...
Reinforcement Learning-Based Controller Reinforcement learning (RL) is an agent-based and model-free machine learning algorithm [84] . ...
doi:10.3390/en14061562
fatcat:6fcls2c75nfwfkvvzxnfobxxfy
Towards Self-Aware Multirotor Formations
2020
Computers
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The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple ...
Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. ...
Several authors present works based on MPC, e.g., hierarchical MPC controllers [16] [17] [18] [19] , leader-follower approaches [20] , a distributed MPC based collision avoidance controller [21] , local ...
doi:10.3390/computers9010007
fatcat:pvmyyrp2hbewvpsakg25zg3rty
Table of Contents
2021
IEEE Robotics and Automation Letters
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Wagner 1304 BADGR: An Autonomous Self-Supervised Learning-Based Navigation System . . . . . . G. Kahn, P. Abbeel, and S. ...
Kheddar 1840 Semi-Supervised Gated Recurrent Neural Networks for Robotic Terrain Classification .
, and N. ...
doi:10.1109/lra.2021.3072707
fatcat:qyphyzqxfrgg7dxdol4qamrdqu
Cooperative control in production and logistics
2015
Annual Reviews in Control
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Standard results as well as recent advances from control theory, through cooperative game theory, distributed machine learning to holonic systems, cooperative enterprise modelling, system integration, ...
Two case studies are also discussed: i) a holonic, PROSA-based approach to generate short-term forecasts for an additive manufacturing system by means of a delegate multi-agent system (D-MAS); and ii) ...
Machine Learning (ML) is divided into 3 main paradigms, namely: (a) supervised learning (such as neural networks, kernel machines, and Bayes classifiers); (b) self-organized or unsupervised learning (such ...
doi:10.1016/j.arcontrol.2015.03.001
fatcat:jiowstx4dvd3phmbxrhy37fr2a
Cooperative Control in Production and Logistics
2014
IFAC Proceedings Volumes
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Standard results as well as recent advances from control theory, through cooperative game theory, distributed machine learning to holonic systems, cooperative enterprise modelling, system integration, ...
Two case studies are also discussed: i) a holonic, PROSA-based approach to generate short-term forecasts for an additive manufacturing system by means of a delegate multi-agent system (D-MAS); and ii) ...
Machine Learning (ML) is divided into 3 main paradigms, namely: (a) supervised learning (such as neural networks, kernel machines, and Bayes classifiers); (b) self-organized or unsupervised learning (such ...
doi:10.3182/20140824-6-za-1003.01026
fatcat:kcbnlnahgjajfbtzqdq2y4ljim
Modelling and control of hybrid electric vehicles (A comprehensive review)
2017
Renewable & Sustainable Energy Reviews
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Index Terms: -Heuristic control, Hybrid electric vehicle, Regenerative braking, Optimization of brake energy recovery, dynamic programming, optimal control, HEV control, vehicle modelling, Parallel HEV ...
The challenge in developing an HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery ...
This form of supervised learning is facilitated by the back propagation method. The adaptive structure of neural network makes it suitable for HEV energy management applications. ...
doi:10.1016/j.rser.2017.01.075
fatcat:dpad3l3svvbpde4waugurx65jm
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