Proportional-Retarded (PR) Protocol for a Large Scale Multi-agent Network with Noisy Measurements; Stability and Performance

2019 ◽  
pp. 249-263
Author(s):  
Adrián Ramírez ◽  
Rifat Sipahi
Keyword(s):  
Large Scale ◽  
Multi Agent ◽  
And Performance ◽  
Noisy Measurements

scholarly journals A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4041
Author(s):  
Anca Maxim ◽  
Constantin-Florin Caruntu
Keyword(s):  
Model Predictive Control ◽  
Communication Networks ◽  
Predictive Control ◽  
Technological Development ◽  
Distributed Model ◽  
Multi Agent Systems ◽  
Test Scenario ◽  
Distributed Model Predictive Control ◽  
Multi Agent ◽  
And Performance

Following the current technological development and informational advancement, more and more physical systems have become interconnected and linked via communication networks. The objective of this work is the development of a Coalitional Distributed Model Predictive Control (C- DMPC) strategy suitable for controlling cyber-physical, multi-agent systems. The motivation behind this endeavour is to design a novel algorithm with a flexible control architecture by combining the advantages of classical DMPC with Coalitional MPC. The simulation results were achieved using a test scenario composed of four dynamically coupled sub-systems, connected through an unidirectional communication topology. The obtained results illustrate that, when the feasibility of the local optimization problem is lost, forming a coalition between neighbouring agents solves this shortcoming and maintains the functionality of the entire system. These findings successfully prove the efficiency and performance of the proposed coalitional DMPC method.

scholarly journals Review of Battery Management Systems (BMS) Development and Industrial Standards

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Hossam A. Gabbar ◽  
Ahmed M. Othman ◽  
Muhammad R. Abdussami
Keyword(s):  
Energy Storage ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Storage System ◽  
Energy Storage System ◽  
Battery Management System ◽  
Battery Management ◽  
Electrical Power System ◽  
Safety Aspects ◽  
And Performance

The evolving global landscape for electrical distribution and use created a need area for energy storage systems (ESS), making them among the fastest growing electrical power system products. A key element in any energy storage system is the capability to monitor, control, and optimize performance of an individual or multiple battery modules in an energy storage system and the ability to control the disconnection of the module(s) from the system in the event of abnormal conditions. This management scheme is known as “battery management system (BMS)”, which is one of the essential units in electrical equipment. BMS reacts with external events, as well with as an internal event. It is used to improve the battery performance with proper safety measures within a system. Therefore, a safe BMS is the prerequisite for operating an electrical system. This report analyzes the details of BMS for electric transportation and large-scale (stationary) energy storage. The analysis includes different aspects of BMS covering testing, component, functionalities, topology, operation, architecture, and BMS safety aspects. Additionally, current related standards and codes related to BMS are also reviewed. The report investigates BMS safety aspects, battery technology, regulation needs, and offer recommendations. It further studies current gaps in respect to the safety requirements and performance requirements of BMS by focusing mainly on the electric transportation and stationary application. The report further provides a framework for developing a new standard on BMS, especially on BMS safety and operational risk. In conclusion, four main areas of (1) BMS construction, (2) Operation Parameters, (3) BMS Integration, and (4) Installation for improvement of BMS safety and performance are identified, and detailed recommendations were provided for each area. It is recommended that a technical review of the BMS be performed for transportation electrification and large-scale (stationary) applications. A comprehensive evaluation of the components, architectures, and safety risks applicable to BMS operation is also presented.

scholarly journals A Confrontation Decision-Making Method with Deep Reinforcement Learning and Knowledge Transfer for Multi-Agent System

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 631
Author(s):  
Chunyang Hu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Knowledge Transfer ◽  
Large Scale ◽  
Effective Control ◽  
Small Scale ◽  
Learning Agent ◽  
Multi Agent ◽  
Transfer Method ◽  
Parameter Sharing

In this paper, deep reinforcement learning (DRL) and knowledge transfer are used to achieve the effective control of the learning agent for the confrontation in the multi-agent systems. Firstly, a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm with parameter sharing is proposed to achieve confrontation decision-making of multi-agent. In the process of training, the information of other agents is introduced to the critic network to improve the strategy of confrontation. The parameter sharing mechanism can reduce the loss of experience storage. In the DDPG algorithm, we use four neural networks to generate real-time action and Q-value function respectively and use a momentum mechanism to optimize the training process to accelerate the convergence rate for the neural network. Secondly, this paper introduces an auxiliary controller using a policy-based reinforcement learning (RL) method to achieve the assistant decision-making for the game agent. In addition, an effective reward function is used to help agents balance losses of enemies and our side. Furthermore, this paper also uses the knowledge transfer method to extend the learning model to more complex scenes and improve the generalization of the proposed confrontation model. Two confrontation decision-making experiments are designed to verify the effectiveness of the proposed method. In a small-scale task scenario, the trained agent can successfully learn to fight with the competitors and achieve a good winning rate. For large-scale confrontation scenarios, the knowledge transfer method can gradually improve the decision-making level of the learning agent.

scholarly journals Hydropower, Geothermal, and Ocean Energy

MRS Bulletin ◽  
2008 ◽  
Vol 33 (4) ◽  
pp. 389-395 ◽  
Author(s):  
Ralph E.H. Sims
Keyword(s):  
Renewable Energy ◽  
Large Scale ◽  
New Technologies ◽  
Materials Science ◽  
Development Stage ◽  
Design Development ◽  
Ocean Energy ◽  
Improve Design ◽  
And Performance ◽  
Improve Reliability

AbstractSome forms of renewable energy have long contributed to electricity generation, whereas others are just emerging. For example, large-scale hydropower is a mature technology generating about 16% of global electricity, and many smaller scale systems are also being installed worldwide. Future opportunities to improve the technology are limited but include upgrading of existing plants to gain greater performance efficiencies and reduced maintenance. Geothermal energy, widely used for power generation and direct heat applications, is also mature, but new technologies could improve plant designs, extend their lifetimes, and improve reliability. By contrast, ocean energy is an emerging renewable energy technology. Design, development, and testing of a myriad of devices remain mainly in the research and development stage, with many opportunities for materials science to improve design and performance, reduce costly maintenance procedures, and extend plant operating lifetimes under the harsh marine environment.

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