Modeling and Analysis of Cooperative Control Systems for Uninhabited Autonomous Vehicles

Finke, Jorge; Passino, K.M.; Ganapathy, Sriram; Sparks, Andrew G.

doi:10.1007/978-3-540-31595-7_5

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…Hence (20) Using (19) and (20), along with (15) yields (21) Because (18) and (21) both bound from below, we can claim that is always bounded from below by the lesser of the two bounds. Therefore, it is always true that (22) Next, we define a constant on which the rest of the proof will depend.…”

Section: A Proof Of Theoremmentioning

confidence: 97%

“…Next, consider how to define . Other ways to define this for different levels of uncertainty are discussed in [20]. Here, we consider the case where there is a high scenario uncertainty, but AAVs have perfect sensor capabilities.…”

Section: Observation and State Equationsmentioning

confidence: 99%

“…Other work that considers network delays in an analytical framework is in [18], where a distributed scheduling approach is used for a specific class of cooperative control problems. A description of our earlier work in this area is provided in [19] and [20].…”

Section: Introductionmentioning

confidence: 99%

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Stable task load balancing strategies for Cooperative control of networked autonomous air vehicles

Finke

Passino

Sparks

2006

IEEE Trans. Contr. Syst. Technol.

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Abstract-We introduce a mathematical model for the study of cooperative control problems for multiple autonomous air vehicles (AAVs) connected via a communication network. We propose a cooperative control strategy based on task-load balancing that seeks to ensure that no vehicle is underutilized and we show how to characterize task-load balancing as a stability property. Then, using Lyapunov stability analysis, we provide conditions under which task-load balancing is achieved even in the presence of communication delays. Finally, we investigate performance properties of the cooperative controller using Monte Carlo simulations. This shows the benefits of cooperation and the effects of network delays and communication topology on performance.

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Section: A Proof Of Theoremmentioning

confidence: 97%

Section: Observation and State Equationsmentioning

confidence: 99%

See 1 more Smart Citation

Stable task load balancing strategies for Cooperative control of networked autonomous air vehicles

Finke

Passino

Sparks

2006

IEEE Trans. Contr. Syst. Technol.

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“…Task allocation problem is discussed in [13][14][15][16][17][18] using different methodologies. Some applications of using a UAV swarm to search and destroy targets could be found in [19][20][21][22]. [23], a swarm simulator for target searching is implemented with Java.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms

Huang

2013

Mathematical Problems in Engineering

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Recently, distributed coordination control of the unmanned aerial vehicle (UAV) swarms has been a particularly active topic in intelligent system field. In this paper, through understanding the emergent mechanism of the complex system, further research on the flocking and the dynamic characteristic of UAV swarms will be given. Firstly, this paper analyzes the current researches and existent problems of UAV swarms. Afterwards, by the theory of stochastic process and supplemented variables, a differential-integral model is established, converting the system model into Volterra integral equation. The existence and uniqueness of the solution of the system are discussed. Then the flocking control law is given based on artificial potential with system consensus. At last, we analyze the stability of the proposed flocking control algorithm based on the Lyapunov approach and prove that the system in a limited time can converge to the consensus direction of the velocity. Simulation results are provided to verify the conclusion.

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“…in terms of arc segments on circles and line segments [5]). All UAVs are connected via communication network [6] and assumed to have perfect knowledge of threats that have been popped up. …”

mentioning

confidence: 99%

Coordinating networked uninhabited air vehicles for persistent area denial

Liu

Cruz

Sparks³

2004

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

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Abstract-This paper explores the problem of cooperative control among multiple networked unmanned air vehicles (UAVs) for persistent area denial (PAD) mission. An adaptive Markov chain model is used to predict the locations of pop-up threats. The mixed information of predicted pop-up threats and actual pop-up targets is utilized to develop cooperative strategies for networked UAVs. The approach is illustrated by use of a simulation test bed for multiple networked UAVs and Monte Carlo simulation runs to evaluate our cooperative strategy. Both theoretical analysis and simulation results are presented to demonstrate the effectiveness of using predicted pop-up information in improving the overall PAD mission performance. I. INTRODUCTIONNINHABITED air vehicles have been identified as potential valuable resources for future military, communications and earth-science efforts. Cooperation among multiple UAVs is a key capability for utilizing the full potential of UAV systems. With respect to military applications, one of the potential missions for UAVs is persistent area denial. The aim of multiple UAVs in PAD operations is to provide persistent surveillance, tracking, and rapid engagement with high-volume strike, against threats (e.g., enemy integrated air defense system) at various ranges in the adversarial terrains. Threats in the battle space, in general, are of two types: known and popup. The locations of known threats are identified by the UAVs at the beginning of the battle, while the locations of pop-up targets are not always observable during the entire PAD operation. In other words, the pop-up targets may appear and disappear at frequent and random intervals of time. Obviously, uncertainty introduced by pop-up targets presents the significant theoretical and technical challenges This work was supported by the AFRL/VA and AFOSR Collaborative Center of Control Science Utilizing a mixed integer programming approach, a cooperative control strategy of multiple UAVs for PAD operation is developed in [2] as well. In this strategy, the trajectories of vehicles are optimized so that they can reach all targets in the shortest time. The predicted information, however, is not utilized in the computation of cooperative control strategies. In this paper, we modify this cooperative strategy in a manner that allows the UAVs to make use of both actual and predicted information about pop-up targets in coordinating their target assignments.Moreover, since recent advances in telecommunication have provided enabling technologies for achieving cooperative control of multiple UAVs via a communication network [3], we will consider networked UAVs in performing the PAD operations. II. SYSTEM MODEL A. Adaptive Markov-Chain Model of Pop-up TargetsPop-up targets are ground assets that appear at unpredictable locations and at random instants of time. In addition, pop-up targets may stay for a regular interval time and then disappear. Suppose that the location of the next pop-up target only depends on the location of present targets in the...

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Modeling and Analysis of Cooperative Control Systems for Uninhabited Autonomous Vehicles

Cited by 7 publications

References 18 publications

Stable task load balancing strategies for Cooperative control of networked autonomous air vehicles

Stable task load balancing strategies for Cooperative control of networked autonomous air vehicles

Modeling and Flocking Consensus Analysis for Large-Scale UAV Swarms

Coordinating networked uninhabited air vehicles for persistent area denial

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