A System Approach to Describing, Analysing and Control of the Behaviour of Agents in MAS

2010 ◽  
pp. 253-273
Author(s):  
František Capkovic
Keyword(s):  
System Analysis ◽  
Single Agent ◽  
State Equation ◽  
Control Synthesis ◽  
System Control ◽  
Multi Agent Systems ◽  
Linear Discrete System ◽  
Multi Agent ◽  
System Properties ◽  
And Control

The Petri nets (PN)-based analytical approach to describing both the single agent behaviour as well as the cooperation of several agents in MAS (multi agent systems) is presented. PN yield the possibility to express the agent behaviour and cooperation by means of the vector state equation in the form of linear discrete system. Hence, the modular approach to the creation of the MAS model can be successfully used too. Three different interconnections of modules (agents, interfaces, environment) expressed by PN subnets are introduced. The approach makes possible to use methods of linear algebra. Moreover, it can be successfully used at the system analysis (e.g. the reachability of states), at testing the system properties, and even at the system control synthesis.

Leader-Following Consensus Control of General Linear Multi-Agent Systems With Diverse Time-Varying Input Delays

2017 ◽  
Vol 140 (6) ◽  
Author(s):  
Chengzhi Yuan
Keyword(s):  
Single Agent ◽  
General Linear ◽  
Control Synthesis ◽  
Time Varying ◽  
Multi Agent Systems ◽  
Consensus Control ◽  
Agent Systems ◽  
Leader Following ◽  
Multi Agent ◽  
Input Delays

This paper addresses the problem of leader-following consensus control of general linear multi-agent systems (MASs) with diverse time-varying input delays under the integral quadratic constraint (IQC) framework. A novel exact-memory distributed output-feedback delay controller structure is proposed, which utilizes not only relative estimation state information from neighboring agents but also local real-time information of time delays and the associated dynamic IQC-induced states from the agent itself for feedback control. As a result, the distributed consensus problem can be decomposed into H∞ stabilization subproblems for a set of independent linear fractional transformation (LFT) systems, whose dimensions are equal to that of a single agent plant plus the associated local IQC dynamics. New delay control synthesis conditions for each subproblem are fully characterized as linear matrix inequalities (LMIs). A numerical example is used to demonstrate the proposed approach.

Dynamic Generation and Control of Optimally Persistent Formation for Multi-agent Systems

2014 ◽  
Vol 39 (9) ◽  
pp. 1431-1438 ◽  
Author(s):  
Xiao-Yuan LUO ◽  
Shi-Kai SHAO ◽  
Xin-Ping GUAN ◽  
Yuan-Jie ZHAO
Keyword(s):  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Dynamic Generation ◽  
And Control

ACCELERATING THE EMERGENCE OF ORDER IN SWARMING SYSTEMS

2019 ◽  
Vol 23 (01) ◽  
pp. 1950015 ◽  
Author(s):  
YANDONG XIAO ◽  
CHULIANG SONG ◽  
LIANG TIAN ◽  
YANG-YU LIU
Keyword(s):  
Large Population ◽  
Unmanned Vehicles ◽  
Global Alignment ◽  
Multi Agent Systems ◽  
Vicsek Model ◽  
Contemporary Science ◽  
Speed Up ◽  
Multi Agent ◽  
Noise Robust ◽  
And Control

Our ability to understand and control the emergence of order in swarming systems is a fundamental challenge in contemporary science. The standard Vicsek model (SVM) — a minimal model for swarming systems of self-propelled particles — describes a large population of agents reaching global alignment without the need of central control. Yet, the emergence of order in this model takes time and is not robust to noise. In many real-world scenarios, we need a decentralized protocol to guide a swarming system (e.g., unmanned vehicles or nanorobots) to reach an ordered state in a prompt and noise-robust manner. Here, we find that introducing a simple adaptive rule based on the heading differences of neighboring particles in the Vicsek model can effectively speed up their global alignment, mitigate the disturbance of noise to alignment, and maintain a robust alignment under predation. This simple adaptive model of swarming systems could offer new insights in understanding the prompt and flexible formation of animals and help us design better protocols to achieve fast and robust alignment for multi-agent systems.

scholarly journals Augmented Reality for Robotics: A Review

Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Zhanat Makhataeva ◽  
Huseyin Varol
Keyword(s):  
Augmented Reality ◽  
Human Robot Interaction ◽  
Multi Agent Systems ◽  
Planning And Control ◽  
Robot Swarms ◽  
Wearable Robots ◽  
Camera Localization ◽  
Recent Developments ◽  
Multi Agent ◽  
And Control

Augmented reality (AR) is used to enhance the perception of the real world by integrating virtual objects to an image sequence acquired from various camera technologies. Numerous AR applications in robotics have been developed in recent years. The aim of this paper is to provide an overview of AR research in robotics during the five year period from 2015 to 2019. We classified these works in terms of application areas into four categories: (1) Medical robotics: Robot-Assisted surgery (RAS), prosthetics, rehabilitation, and training systems; (2) Motion planning and control: trajectory generation, robot programming, simulation, and manipulation; (3) Human-robot interaction (HRI): teleoperation, collaborative interfaces, wearable robots, haptic interfaces, brain-computer interfaces (BCIs), and gaming; (4) Multi-agent systems: use of visual feedback to remotely control drones, robot swarms, and robots with shared workspace. Recent developments in AR technology are discussed followed by the challenges met in AR due to issues of camera localization, environment mapping, and registration. We explore AR applications in terms of how AR was integrated and which improvements it introduced to corresponding fields of robotics. In addition, we summarize the major limitations of the presented applications in each category. Finally, we conclude our review with future directions of AR research in robotics. The survey covers over 100 research works published over the last five years.

scholarly journals Impulsive Consensus Tracking of Multiagent Systems with Quantization and Input Delays Using Position-Only Information

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hong Zhou ◽  
Yu-Zhen Qin ◽  
Kuo Feng ◽  
Wen-Shan Hu ◽  
Zhi-Wei Liu
Keyword(s):  
Sampling Period ◽  
Second Order ◽  
Quantization Scheme ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Sufficient And Necessary Conditions ◽  
Multi Agent ◽  
Input Delays ◽  
And Control

This paper investigates the consensus tracking problem for second-order multi-agent systems without/with input delays. Randomized quantization scheme is considered in the communication channels, and impulsive consensus tracking algorithms using position-only information are proposed for the consensus tracking of multi-agent systems. Based on the algebraic graph theory and stability theory of impulsive systems, sufficient and necessary conditions for consensus tracking are studied. It is found that consensus tracking for second-order multi-agent systems without/with input delays can be achieved by appropriately choosing the sampling period and control gains which are determined by second/third degree polynomials. Simulations are performed to validate the theoretical results.

The Effect of Reward Interdependence of Strategies in Evolutionary Multi-Agent Systems

2009 ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Behavior ◽  
System Analysis ◽  
Three Dimensional ◽  
Computational Design ◽  
Design Teams ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Effective Strategies ◽  
Multi Agent ◽  
Potential Impact

This paper explores the effect of reward interdependence of strategies in a cooperative evolving team on the performance of the team. Experiments extending the Evolutionary Multi-Agent Systems (EMAS) framework to three dimensional layout are designed which examine the effect of rewarding helpful, in addition to effective strategies on the convergence of the system. Analysis of communication within the system suggests that some agents (strategies) are more effective at creating helpful solutions than creating good solutions. Despite their potential impact as enablers for other strategies, when their efforts were not rewarded, these assistant agent types were quickly removed from the population. When reward was interdependent, however, this secondary group of helpful agents remained in the population longer. As a result, effective communication channels remained open and the system converged more quickly. The results support conclusions of organizational behavior experimentation and computational modeling. The implications of this study are twofold. First, computational design teams may be made more effective by recognizing and rewarding indirect contributions of some strategies to the success of others. Secondly, EMAS may provide a platform for predicting the effectiveness of different reward structures given a set of strategies in both human and computational teams.

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