Multi-agent Optimization Algorithms for a Single Class of Optimal Deterministic Control Systems

2020 ◽  
pp. 271-291
Author(s):  
Andrei V. Panteleev ◽  
Maria Magdalina S. Karane
Keyword(s):  
Control Systems ◽  
Optimization Algorithms ◽  
Single Class ◽  
Multi Agent ◽  
Deterministic Control

scholarly journals Drone Swarms as Networked Control Systems by Integration of Networking and Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2642
Author(s):  
Godwin Asaamoning ◽  
Paulo Mendes ◽  
Denis Rosário ◽  
Eduardo Cerqueira
Keyword(s):  
Control Systems ◽  
Networked Control Systems ◽  
Cooperative Behavior ◽  
Networked Control ◽  
Multi Agent Systems ◽  
Wireless Communication Network ◽  
Self Organized ◽  
Design Choice ◽  
Multi Agent ◽  
Computational Systems

The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.

Multi-Agent Optimization Design for Autonomous Lagrangian Systems

2016 ◽  
Vol 04 (01) ◽  
pp. 5-13 ◽  
Author(s):  
Zhenhua Deng ◽  
Yiguang Hong
Keyword(s):  
Continuous Time ◽  
Optimization Design ◽  
Exponential Convergence ◽  
Distributed Optimization ◽  
Optimization Algorithms ◽  
Lagrangian Systems ◽  
Uncertain Parameters ◽  
Optimization Task ◽  
Optimization Control ◽  
Multi Agent

In this paper, distributed optimization control for a group of autonomous Lagrangian systems is studied to achieve an optimization task with local cost functions. To solve the problem, two continuous-time distributed optimization algorithms are designed for multiple heterogeneous Lagrangian agents with uncertain parameters. The proposed algorithms are proved to be effective for those heterogeneous nonlinear agents to achieve the optimization solution in the semi-global sense, even with the exponential convergence rate. Moreover, simulation adequately illustrates the effectiveness of our optimization algorithms.

scholarly journals Design and Analysis of Optimization Algorithms for Multi-agent Railway Control System

2014 ◽  
Vol 37 ◽  
pp. 211-219 ◽  
Author(s):  
N.A. Kuznetsov ◽  
I.K. Minashina ◽  
F.F. Pashchenko ◽  
N.G. Ryabykh ◽  
E.M. Zakharova
Keyword(s):  
Control System ◽  
Optimization Algorithms ◽  
Multi Agent

scholarly journals Approximate Controllability of Sobolev Type Nonlocal Fractional Stochastic Dynamic Systems in Hilbert Spaces

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mourad Kerboua ◽  
Amar Debbouche ◽  
Dumitru Baleanu
Keyword(s):  
Control Systems ◽  
Hilbert Spaces ◽  
Dynamic Systems ◽  
Approximate Controllability ◽  
Dynamic Control ◽  
Sufficient Conditions ◽  
Sobolev Type ◽  
Stochastic Dynamic ◽  
Deterministic Control ◽  
Stochastic Dynamic Systems

We study a class of fractional stochastic dynamic control systems of Sobolev type in Hilbert spaces. We use fixed point technique, fractional calculus, stochastic analysis, and methods adopted directly from deterministic control problems for the main results. A new set of sufficient conditions for approximate controllability is formulated and proved. An example is also given to provide the obtained theory.

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