Multi-Objective Decentralized Model Predictive Control for Cooperative Multi-UAV Systems

Ilaya, Omar

doi:10.2514/6.2007-6585

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Exploring the synergy between cooperative localization, specifically employing simple and efficient Kalman filters [30], and advanced control strategies holds immense potential for achieving enhanced localization accuracy in multi-vehicle systems. Various strategies such as decentralized control frameworks [31], trajectory tracking [32], consensus algorithms [33], and swarm-based guidance systems [3] emphasize the pivotal role of cooperative localization in augmenting autonomous navigation capabilities in multi-vehicle networks.…”

Section: Related Workmentioning

confidence: 99%

Multi-Vehicle Navigation Using Cooperative Localization

Oliveros,

Ashrafiuon

2023

Electronics

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This paper assesses the effectiveness of cooperative localization for improving the performance of closed-loop control systems for networks for autonomous multi-vehicle navigation. Nonlinear dynamic models of two- and three-dimensional vehicles are presented along with their linearized forms. A nonlinear control algorithm is then presented based on the dynamic model. Relative position measurement equations and their linearized forms are introduced. The state and measurement equations are then employed for the propagation and update steps of an EKF-based cooperative localization algorithm. Initially, a series of experiments with networks of quadcopters and mobile robots are presented to validate the performance of cooperative localization for state estimation with the continuous or intermittent presence of absolute measurements or their complete absence. Finally, the performance of the control algorithm is evaluated with and without cooperative localization to demonstrate its effectiveness for improving performance.

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Section: Related Workmentioning

confidence: 99%

Multi-Vehicle Navigation Using Cooperative Localization

Oliveros,

Ashrafiuon

2023

Electronics

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“…Ilaya [84] proposed the use of a decentralized control scheme involving multiple vehicles performing a multi-objective trajectory tracking and consensus problem using particle swarm optimization. The approach incorporated a two-level decision process: a high-level supervisory level and a local vehicle control level.…”

Section: Multiple Objectivementioning

confidence: 99%

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

2016

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Abstract:The use of unmanned aerial systems (UASs) in both the public and military environments is predicted to grow significantly. As the demand for UASs grows, the availability of more robust and capable vehicles that can perform multiple mission types will be needed. In the public sector, the demand will grow for UASs to be used for agriculture, forestry, and search and rescue missions. Militaries continue to demand more UAS capabilities for diverse operations around the world. Significant research has been performed and continues to progress in the areas of autonomous UAS control. A majority of the work focuses on subsets of UAS control: path planning, autonomy, small UAS controls, and sensors. Minimal work exists on a system-level problem of multiple-scenario UAS control for integrated systems. This paper provides a high-level modular system architecture definition that is modifiable across platform types and mission requirements. A review of the current research and employment of UAS capabilities is provided to evaluate the state of the capabilities required to enable the proposed architecture.

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Hamiltonian surface shaping with information theory and exergy/entropy control for collective plume tracing

Robinett

Wilson

2010

IJSCC

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Multi-Objective Decentralized Model Predictive Control for Cooperative Multi-UAV Systems

Cited by 3 publications

References 26 publications

Multi-Vehicle Navigation Using Cooperative Localization

Multi-Vehicle Navigation Using Cooperative Localization

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

Hamiltonian surface shaping with information theory and exergy/entropy control for collective plume tracing

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