A General Stability Condition for Multi-Agent Coordination by Coupled Estimation and Control

2007 ◽  
Author(s):  
Peng Yang ◽  
Randy A. Freeman ◽  
Kevin M. Lynch
Keyword(s):  
Stability Condition ◽  
General Stability ◽  
Multi Agent ◽  
Estimation And Control ◽  
And Control ◽  
Agent Coordination

Multi-Agent Coordination by Decentralized Estimation and Control

2008 ◽  
Vol 53 (11) ◽  
pp. 2480-2496 ◽  
Author(s):  
Peng Yang ◽  
Randy A. Freeman ◽  
Kevin M. Lynch
Keyword(s):  
Decentralized Estimation ◽  
Multi Agent ◽  
Estimation And Control ◽  
And Control ◽  
Agent Coordination

Multi-agent coordination and control using stigmergy

2004 ◽  
Vol 53 (1) ◽  
pp. 75-96 ◽  
Author(s):  
Hadeli ◽  
Paul Valckenaers ◽  
Martin Kollingbaum ◽  
Hendrik Van Brussel
Keyword(s):  
Multi Agent ◽  
And Control ◽  
Agent Coordination ◽  
Coordination And Control

scholarly journals Nonlinear Attitude and Formation Estimation Spacecraft And Multi-Agent Systems

2021 ◽  
Author(s):  
Michael Rososhansky
Keyword(s):  
Particle Filter ◽  
Nonlinear Filtering ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Multi Agent ◽  
Filtering Technique ◽  
Estimation And Control ◽  
And Control

This dissertation examines the state and parameter estimation problem of monolithic spacecraft and multi-agent systems in conjunction with the control algorithms. Nonlinear filtering techniques are investigated and applied to the problems of attitude estimation and control of monolithic spacecraft, distributed flltering for attitude estimation and control of satellite formation flying (SFF), and estimation and control of a multi-agent system in consensus tracking with uncertain dynamic model. The main objective is to investigate the performance of nonlinear filtering techniques under fault-free and fault-prone scenarios. In essence, the core of this research has been placed on identifying techniques to improve the efficiency and reduce the variance of estimations in nonlinear filtering. The research is primarily dedicated to the investigation of adaptive unscented Kalman Filter (AUKF) and particle Filter (PF). A nonlinear filtering technique has been proposed for sequential joint estimation of a multi-agent system in consensus tracking with uncertain dynamic model. The new filter is called marginalized unscented particle Filter (MUPF). The proposed filter uses the Rao-Blackwellised principle to couple the particle filtering technique with unscented transform algorithm

Introduction to the Special Issue on Multi-agent Coordination and Control

2020 ◽  
Vol 17 (8) ◽  
pp. 370-370
Author(s):  
David W. Casbeer ◽  
Satyanarayana G. Manyam ◽  
Rajnikant Sharma
Keyword(s):  
Special Issue ◽  
Multi Agent ◽  
And Control ◽  
Agent Coordination ◽  
Coordination And Control

scholarly journals Nonlinear Attitude and Formation Estimation Spacecraft And Multi-Agent Systems

2021 ◽  
Author(s):  
Michael Rososhansky
Keyword(s):  
Particle Filter ◽  
Nonlinear Filtering ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Consensus Tracking ◽  
Multi Agent ◽  
Filtering Technique ◽  
Estimation And Control ◽  
And Control

This dissertation examines the state and parameter estimation problem of monolithic spacecraft and multi-agent systems in conjunction with the control algorithms. Nonlinear filtering techniques are investigated and applied to the problems of attitude estimation and control of monolithic spacecraft, distributed flltering for attitude estimation and control of satellite formation flying (SFF), and estimation and control of a multi-agent system in consensus tracking with uncertain dynamic model. The main objective is to investigate the performance of nonlinear filtering techniques under fault-free and fault-prone scenarios. In essence, the core of this research has been placed on identifying techniques to improve the efficiency and reduce the variance of estimations in nonlinear filtering. The research is primarily dedicated to the investigation of adaptive unscented Kalman Filter (AUKF) and particle Filter (PF). A nonlinear filtering technique has been proposed for sequential joint estimation of a multi-agent system in consensus tracking with uncertain dynamic model. The new filter is called marginalized unscented particle Filter (MUPF). The proposed filter uses the Rao-Blackwellised principle to couple the particle filtering technique with unscented transform algorithm

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