Predictive control strategy for multi-agent relay tracking systems with time delays

2021 ◽  
pp. 014233122110295
Author(s):  
Lijing Dong ◽  
Fengjin Wei
Keyword(s):  
Target Tracking ◽  
Predictive Control ◽  
Time Delays ◽  
Control Method ◽  
Tracking System ◽  
Multi Agent Systems ◽  
Predictive Values ◽  
Voronoi Cells ◽  
Multi Agent ◽  
Tracking Time

This paper investigates the multi-agent systems for target tracking applications, in which the target is cooperatively relay tracked by multiple agents based on the Voronoi diagram. The relay tracking scheme is effective and reduces the tracking time of a target. However, the existence of time delays results in inaccurate determination of boundaries of Voronoi cells. Thus, the replacement of tracking agent is no longer optimal, which consequently affects the tracking time. In order to solve this issue, a predictive controller is proposed for the relay tracking system with time delays. Moreover, the boundaries of Voronoi cells are determined with the predictive values of agents’ information. In order to analyze the stability of the system, an impulse-time-dependent Lyapunov function is designed. Then, the orthogonal polynomials based inequality is applied and the average dwell time switching technique is adopted to solve the difficulty of stability analysis of the overall relay tracking system caused by the agent replacements of time-delay agents. Finally, the effectiveness and advantages of the proposed predictive control method for relay target tracking system with time delays are illustrated by comparative simulations.

scholarly journals Boltzmann Distributed Replicator Dynamics: Population Games in a Microgrid Context

Games ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Gustavo Chica-Pedraza ◽  
Eduardo Mojica-Nava ◽  
Ernesto Cadena-Muñoz
Keyword(s):  
Control Method ◽  
Optimization Problems ◽  
Replicator Dynamics ◽  
Boltzmann Distribution ◽  
Full Information ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Population Games ◽  
Distributed Approach ◽  
Multi Agent

Multi-Agent Systems (MAS) have been used to solve several optimization problems in control systems. MAS allow understanding the interactions between agents and the complexity of the system, thus generating functional models that are closer to reality. However, these approaches assume that information between agents is always available, which means the employment of a full-information model. Some tendencies have been growing in importance to tackle scenarios where information constraints are relevant issues. In this sense, game theory approaches appear as a useful technique that use a strategy concept to analyze the interactions of the agents and achieve the maximization of agent outcomes. In this paper, we propose a distributed control method of learning that allows analyzing the effect of the exploration concept in MAS. The dynamics obtained use Q-learning from reinforcement learning as a way to include the concept of exploration into the classic exploration-less Replicator Dynamics equation. Then, the Boltzmann distribution is used to introduce the Boltzmann-Based Distributed Replicator Dynamics as a tool for controlling agents behaviors. This distributed approach can be used in several engineering applications, where communications constraints between agents are considered. The behavior of the proposed method is analyzed using a smart grid application for validation purposes. Results show that despite the lack of full information of the system, by controlling some parameters of the method, it has similar behavior to the traditional centralized approaches.

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.

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