The aim of this work is to develop a robust control strategy able to drive the attitude of a spacecraft to a reference value,despite the presence of unknown but bounded uncertainties in the system parameters and exter...The aim of this work is to develop a robust control strategy able to drive the attitude of a spacecraft to a reference value,despite the presence of unknown but bounded uncertainties in the system parameters and external disturbances.Thanks to the use of an extended observer design,the proposed control law is robust against all the uncertainties that affect the high-frequency gain matrix,which is shown to capture a broad spectrum of modelling issues,some of which are often neglected by traditional approaches.The proposed controller then provides robustness against parametric uncertainties,as moment of inertia estimation,payload deformations,actuator faults and external disturbances,while maintaining its asymptotic properties.展开更多
Federated reinforcement learning(FedRL)is an emerging paradigm in data-driven control where a group of decision-mak-ing agents cooperate to learn optimal control laws through a distributed reinforcement learning proce...Federated reinforcement learning(FedRL)is an emerging paradigm in data-driven control where a group of decision-mak-ing agents cooperate to learn optimal control laws through a distributed reinforcement learning procedure,with the peculiarity of hav-ing the constraints of not sharing any process/control data.In the typical FedRL setting,a centralized entity is responsible for orches-trating the distributed training process.To remove this design limitation,this work proposes a solution to enable a fully decentralized approach leveraging on results from consensus theory.The proposed algorithm,named FedRLCon,can then deal with:1)scenarios with homogeneous agents,which can share their actor and,possibly,the critic networks;2)scenarios with heterogeneous agents,in which agents may share their critic network only.The proposed algorithms are validated on two scenarios,consisting of a resource manage-ment problem in a communication network and a smart grid case study.Our tests show that practically no performance is lost for the decentralization.展开更多
This paper proposes a deep-Q-network(DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process(MDP) model of the problem. Ne...This paper proposes a deep-Q-network(DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process(MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT(radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load balancing.In particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.展开更多
Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training clients.FedL was proposed for ...Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training clients.FedL was proposed for edge computing and Internet of things(IoT)tasks in which a centralized server was responsible for coordinating and governing the training process.To remove the design limitation implied by the centralized entity,this work proposes two different solutions to decentralize existing FedL algorithms,enabling the application of FedL on networks with arbitrary communication topologies,and thus extending the domain of application of FedL to more complex scenarios and new tasks.Of the two proposed algorithms,one,called FedLCon,is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solutions,as also shown by the reported validation tests.展开更多
文摘The aim of this work is to develop a robust control strategy able to drive the attitude of a spacecraft to a reference value,despite the presence of unknown but bounded uncertainties in the system parameters and external disturbances.Thanks to the use of an extended observer design,the proposed control law is robust against all the uncertainties that affect the high-frequency gain matrix,which is shown to capture a broad spectrum of modelling issues,some of which are often neglected by traditional approaches.The proposed controller then provides robustness against parametric uncertainties,as moment of inertia estimation,payload deformations,actuator faults and external disturbances,while maintaining its asymptotic properties.
文摘Federated reinforcement learning(FedRL)is an emerging paradigm in data-driven control where a group of decision-mak-ing agents cooperate to learn optimal control laws through a distributed reinforcement learning procedure,with the peculiarity of hav-ing the constraints of not sharing any process/control data.In the typical FedRL setting,a centralized entity is responsible for orches-trating the distributed training process.To remove this design limitation,this work proposes a solution to enable a fully decentralized approach leveraging on results from consensus theory.The proposed algorithm,named FedRLCon,can then deal with:1)scenarios with homogeneous agents,which can share their actor and,possibly,the critic networks;2)scenarios with heterogeneous agents,in which agents may share their critic network only.The proposed algorithms are validated on two scenarios,consisting of a resource manage-ment problem in a communication network and a smart grid case study.Our tests show that practically no performance is lost for the decentralization.
基金supported by the European Commission in the framework of the H2020 EU-Korea project 5GALLSTAR(5G Agi Le and f Lexible integration of Sa Tellite And cellula R,www.5g-allstar.eu)(No.815323)。
文摘This paper proposes a deep-Q-network(DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process(MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT(radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load balancing.In particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.
基金Supported by the Lazio region,in the scope of the project FedMedAI,Regional Operative Prgramme (POR) of the European fund for regional development (FESR) Lazio 2014–2020 (Azione 1.2.1)(No.A0375-2020-36491-23/10/2020)
文摘Federated learning(FedL)is a machine learning(ML)technique utilized to train deep neural networks(DeepNNs)in a distributed way without the need to share data among the federated training clients.FedL was proposed for edge computing and Internet of things(IoT)tasks in which a centralized server was responsible for coordinating and governing the training process.To remove the design limitation implied by the centralized entity,this work proposes two different solutions to decentralize existing FedL algorithms,enabling the application of FedL on networks with arbitrary communication topologies,and thus extending the domain of application of FedL to more complex scenarios and new tasks.Of the two proposed algorithms,one,called FedLCon,is developed based on results from discrete-time weighted average consensus theory and is able to reconstruct the performances of the standard centralized FedL solutions,as also shown by the reported validation tests.
