جستجوی مقالات مرتبط با کلیدواژه "سیستم های چندعاملی" در نشریات گروه "برق"

Utilizing the structure of multi-agent systems is one of the methods proposed today to improve the performance of network protection systems. Given that the presence of distributed generation resources as well as network restructuring has always been a concern for network managers, so it is expected that with the introduction of smart equipment and capabilities that provide network managers, better performance and maintenance They help. Intelligent devices are able to exchange information by communicating on a telecommunications platform. The purpose of this paper is to coordinate the protection relays in the presence of distributed generation sources by removing the connection between the relay agents and the information collected from the entire network by the central controller. The proposed method, in addition to improving the speed of the traditional multi agent system protection system, increases the security of the protection system and eliminates the need for information from one agent of one section to the agents of another sections.

In this paper, a new energy management strategy is presented by using shunt active power filter (SAPF) in a multi-agent structure. This strategy is applied to a micro-grid connected to the grid and includes the problem of harmonic compensation. By examining the advantages and disadvantages of shunt active power filters and passive filters, as well as their efficiency in the multi-agent structure for power micro-grids, the reason for using shunt active power filters in the proposed method has been determined. Also, the performance of these filters for compensating current harmonics has been compared by examining the FFT results. In the used micro-grid, wind turbine generator and solar cell generator are used as renewable energy sources (RES) and two fuel cells are used to compensate for sudden and unplanned changes in the production power of these two generators. The energy management unit manages the active and inactive state of the two fuel cells according to the production power and consumption power of the micro-grid in such a way that the power exchanged between the micro-grid and the main grid is limited within an acceptable range. The simulation results show that the proposed method using local continuous controllers (in each agent) and central discrete controller (energy management system) has been able to perform well and while providing the required power of the micro-grid, at the same time, it performs the current harmonics compensation issue correctly.

Recent researches show that diverse systems in many different areas can be represented as complex networks. Examples of these include the Internet, social networks and so on. In each case, the system can be modeled as a complex and very large network consisting of a large number of entities and associations between them. Most of these networks are generally sparse in global yet dense in local. They have vertices in a group structure and the vertices within a group have higher density of edges while vertices among groups have lower density of edges. Such a structure is called community and is one of the important features of the network and is able to reveal many hidden characteristics of the networks. Today, community detection is used to improve the efficiency of search engines and discovery of terrorist organizations on the World Wide Web. Community detection is a challenging NP-hard optimization problem that consists of searching for communities. It is assumed that the nodes of the same community share some properties that enable the detection of new characteristics or functional relationships in a network. Although there are many algorithms developed for community detection, most of them are unsuitable when dealing with large networks due to their computational cost. Nowadays, multiagent systems have been used to solve different problems, such as constraint satisfaction problems and combinatorial optimization problems with satisfactory results. In this paper, a new multiagent reinforcement learning algorithm is proposed for community detection in complex networks. Each agent in the multiagent system is an autonomous entity with different learning parameters. Based on the cooperation among the learning agents and updating the action probabilities of each agent, the algorithm interactively will identify a set of communities in the input network that are more densely connected than other communities. In other words, some independent agents interactively attempt to identify communities and evaluate the quality of the communities found at each stage by the normalized cut as objective function; then, the probability vectors of the agents are updated based on the results of the evaluation. If the quality of the community found by an agent in each of the stages is better than all the results produced so far, then it is referred to as the successful agent and the other agents will update their probability vectors based on the result of the successful agent. In the experiments, the performance of the proposed algorithm is validated on four real-world benchmark networks: the Karate club network, Dolphins network, Political books network and College football network, and synthetic LFR benchmark graphs with scales of 1000 and 5000 nodes. LFR networks are suitable for systematically measuring the property of an algorithm. Experimental results show that proposed approach has a good performance and is able to find suitable communities in large and small scale networks and is capable of detecting the community in complex networks In terms of speed, precision and stability. Moreover, according to the systematic comparison of the results obtained by the proposed algorithm with four state-of-the-art community detection algorithms, our algorithm outperforms the these algorithms in terms of modularity and NMI; also, it can detect communities in small and large scale networks with high speed, accuracy, and stability, where it is capable of managing large-scale networks up to 5000 nodes.

This paper considers the design of observer-based distributed adaptive controllers to achieve a leader-follower consensus for high-order nonlinear multi-agent systems in the presence of non-symmetric input saturation constraint and system uncertainties. Solving the consensus problem for nonlinear multi agent systems in the presence of unknown terms in the dynamics equations of follower that are due to model simplification, parameters uncertainty or external disturbances are investigated. In order to reduce the conservatism, the upper bound of uncertain term is considered to be unknown, which is obtained by adaptive laws. In addition, it is assumed that all states variables of agents are not directly measurable; therefore firstly, by designing the nonlinear distributed observers, the states variables of agents are estimated. Then, by using the sliding mode technique, the observer-based distributed adaptive control laws are designed to ensure the consensus between the agents, and the output of the followers can track the output of the leader, in the presence of non-symmetric input saturation and uncertain terms. Finally, the results of the simulations have completely confirmed the achievements of the proposed laws.

This paper has been devoted to the design of a distributed consensus control for a class of uncertain nonlinear multi-agent systems in the strict-feedback form. The communication between the agents has been described by a directed graph. Radial-basis function neural networks have been used for the approximation of the uncertain and heterogeneous dynamics of the followers as well as the effect of the neighbors of each agent in the multi-agent system. Then, the proposed scheme based on the dynamic surface control approach has been presented. Stability analysis of the closed-loop system shows that all the signals of the closed-loop system are uniformly ultimately bounded. The proposed scheme solves the consensus problem in the multi-agent systems with uncertain dynamics and avoids the "explosion of complexity" problem. The simulation results of the proposed approach are presented on a group of single-link robots with uncertain dynamics including four followers and one leader. The presented results verify the effectiveness of the proposed method.

In this paper an online optimal distributed algorithm is introduced for multi-agent systems synchronization under unknown dynamics based on approximate dynamic programming and neural networks. Every agent has employed an actor-critic structure to learn its distributed optimal policy and the unknown dynamics of every agent is identified by employing a neural network approximator. The unknown dynamics are identified based on the experience replay technique where the recorded data and current data are used to adopt the approximators weights. The introduced algorithm learns the solution of coupled Hamilton-Jacobi equations under unknown dynamics in an online fashion. While the weights of the identifiers and actor-critic approximators are being tuned, the boundedness of the closed loop system signals are assured using Lyapunov theory. The effectiveness of the proposed algorithm is shown through the simulation results.

Considering the increasing occurrences of unexpected events and the need for pre-crisis planning in order to reduce risks and losses, modeling instant response environments is needed more than ever. Modeling may lead to more careful planning for crisis-response operations, such as team formation, task assignment, and doing the task by teams. A common challenge in this way is that the model should be understandable for crisis managers, such that they could exploit from the consequences of modeling. To run the model and view the results, the model should be converted to a program. The crisis manager would run the program to see how the model operations, including team formation, task allocation, and task performance, are done. In this paper, the executable code of a multi agent system is automatically generated from a model which is designed based on model driven approach. A domain specific modeling language named ERE-ML and its related tool are used, and some new features are added to this language. To evaluate conversion code output, the case study of Bam earthquake is implemented, and the scenarios defined in the system are visualized.

Using intelligent methods is one of the efficient methods of scheduling the traffic lights at intersections to control traffic urban. In this paper used an clustering and genetic algorithms to control urban traffic at intersections based on multi-agent system. Proposed system get traffic load of each intersection. Next, uses clustering algorithm to find adjacent intersections. Then, it uses evolutionary computing algorithms to scheduling the traffic lights and also it has used exchange of messages between different agents to control possible fluctuations.The main capabilities and advantages of proposed method for simulating a multi-agent system for intelligent urban traffic control at the intersection are as follows: The number of intersection is considered infinite. Also, the distance between intersections is not limited and adjustable. Furthermore,this method is applicable to multi-ways. Due to use Multiagent technique and clustering algorithm for intersections, proposed method has been performed distributed processing which avoid excessive computational load processing in each cycle of traffic lights. For the design and implementation proposed method used Tropos methodology and JADE library. For evaluation, this proposed system tested with 1200 laboratory data in both low and heavy traffic areas. In comparison with fixed-time systems, the average run-time in sequential cycles of intersections in our combinational method is less than using the other single methods. In addition, for heavy traffic area and low traffic area, our system respectively has 18.5% and 30.8% (in average) improvement in delay time of vehicles, compared to the fixed-time methods.