Hierarchical Design for Position-Based Formation Control of Rotorcraft-Like Aerial Vehicles

Zhang, Dandan; Tang, Yang; Zhang, Wenbing; Wu, Xiaotai

doi:10.1109/tcns.2020.3000738

Cited by 29 publications

(13 citation statements)

References 43 publications

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“…Formation control is one of the most popular control problems in MAS (see [4] for a comprehensive review). Stateof-the-art methods include [13], which proposes a positionbased controller for vehicles subject to second-order dynamics; [14] proposes a similar position-based strategy but for aerial vehicles. Representative examples of displacementbased controllers include [15] which presents a method to coordinate autonomous unmanned vehicles (AUVs) with optical sensors, and [16] which studies the maneuvering and robustness of undirected control topologies.…”

Section: A Related Workmentioning

confidence: 99%

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Zhang¹,

Zhi²,

Romero³

et al. 2023

Preprint

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<p>The automatic monitoring/tracking of environmental boundaries by multi-agent systems is a fundamental problem which has many practical applications. In this paper, we address this problem with formation control techniques based on parametric curves that represent the boundary’s feedback shape. For that, we approximate the curve with truncated Fourier series, whose finite coefficients are utilized to characterize the curve’s shape and to automatically distribute the agents along it. These feedback Fourier coefficients are exploited to design a new type of formation controller that drives the agents to form desired curves. A detailed stability analysis is provided for the proposed control methodology, considering both fixed and switching multi-agent topologies. The reported numerical simulation study demonstrates the performance and feasibility of our new method to track closed boundaries of different shapes.</p>

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Section: A Related Workmentioning

confidence: 99%

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Zhang¹,

Zhi²,

Romero³

et al. 2023

Preprint

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“…1940Dec. -1950 Wen, C., see Li, G., TCNS June 2020 673-685 Wen, C., see 1559-1570 Wen, C., see 1308-1318 Wen, C., see Yan, J., TCNS Dec. 20201951-1959 Wen, G., see Hu, H., TCNS June 2020 783-796 Wen, G., see Hong, H., TCNS March 2020 42-52 Wen, G., see Lv, Y., TCNS March 2020 118-128 Wen, G., see Wang, P., TCNS March 2020 254-265 Wen, G., see 1127-1139 Wickenheiser, A.M., see Crandall, K.L., TCNS March 2020 210-220 Wigren, T., Lau, K., Delgado, R.A., and Middleton, R.H., Globally Stable Delay Alignment for Feedback Control Over Wireless Multipoint Connections; TCNS 1633-1642., see 1801-1811 of Natural Gas System via Monotone Inner Polytope Sequence; TCNS June 2020 660-672 Wu, L., see Xiong, Y., 1366-1378 Wu, S., Ren, X., Jia, Q., Johansson, K.H., and Shi, L., Learning Optimal Sched-uling Policy for Remote State Estimation Under Uncertain Channel Condition; TCNS June 2020 579-591 Wu, S., Ding, K., Cheng, P., and Shi, L., Optimal Scheduling of Multiple Sensors Over Lossy and Bandwidth Limited Channels; TCNS Sept. 2020 1188-1200 Wu, X., see Zhang, D., TCNS Dec. 20201789-1800 Wu, Z., see Li, B., TCNS March 2020 201-209 X Xiang, L., Wang, P., Chen, F., and Chen, G., Controllability of Directed Net-worked MIMO Systems With Heterogeneous Dynamics; TCNS June 2020 807-817 Xiao, G., see Zhai, C., TCNS June 2020 956-966 Xiao, G., see Li, G., TCNS June 2020 673-685 Xie, L., see Cao, K., TCNS June 2020 912-922 Xie, L., see Li, X., TCNS March 2020 74-84 Xie, P., see You, K., 1379…”

Section: Time Decision For Multi-input and Multi-outputmentioning

confidence: 99%

“…T Taha, A.F., see 1151-1163 Talukdar, S., see Prakash, M., TCNS March 2020 85-95 Tan, X., see 1416-1427 Tan, Y., see Premaratne, U., TCNS June 2020 758-769 Tang, P., see Li, G., TCNS June 2020 673-685 Tang, Y., see Li, F., 1523-1533 + Check author entry for coauthors Tang, Y., see Zhang, D., TCNS Dec. 20201789-1800 Tannenbaum, A., see Chen, Y., TCNS June 2020 923-931 Tao, Y., see Wang, C., TCNS June 2020 558-567 Taylor, J.A., see Lesage-Landry, A., TCNS June 2020 724-733 Tesi, P., see Roy, S., TCNS March 2020 187-188 Tesi, P., see Shi, M., TCNS Sept. 20201534-1546 Tian, Y., see 1559-1570 Touati, C., see Chen, J., TCNS March 2020 398-409 Towsley, D., see Zafari, F., TCNS March 2020 151-162 Turitsyn, K., see Wu, D., TCNS June 2020 660-672 1319-1329 Van Scoy, B., see Sundararajan, A., TCNS Dec. 20201597-1608 Global Orientation Estimation; TCNS Dec. 2020 1654-1664 Varaiya, P., see Ouyang, Y., 1176-1187 Vasconcelos, M.M., and Mitra, U., Observation-Driven Scheduling for Remote Estimation of Two Gaussian Random Variables; TCNS March 2020 232-244 Vasconcelos, M.M., Gagrani, M., Nayyar, A., and Mitra, U., Optimal Schedul-ing Strategy for Networked Estimation With Energy Harvesting; TCNS Dec. 2020 1723-1735 Venkitasubramaniam, P., see Zhang, R., TCNS March 2020 338-348 Vilgelm, M., see Klugel, M., 1903-1915 W Wahi, P., see 1069-1079 Wan, C., Jing, G., You, S., and Dai, R., Sensor Network Localization via Alternating Rank Minimization Algorithms; TCNS June 2020 1040-1051 Wan, Y., see…”

mentioning

confidence: 99%

2020 Index IEEE Transactions on Control of Network Systems Vol. 7

2020

IEEE Trans. Control Netw. Syst.

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“…Such a cooperative platform is more capable of sharing resources and more robust against failures in team members or in communication links [4][5][6][7]. Adopting a cooperative control strategy for a team of Unmanned Aerial Vehicles (UAVs) improves their capabilities and enables them to autonomously involve in cooperative missions such as formation control [8][9][10]. Formation control is a typical cooperative task in which several agents move with a relatively fixed distance [11,12], while avoiding collision [13].…”

Section: Introductionmentioning

confidence: 99%

Decentralized modular hybrid supervisory control for the formation of unmanned helicopters

Karimoddini

Karimadini

Lin

2022

IET Control Theory & Appl

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Formation control of Unmanned Aerial Vehicles (UAVs) requires them to tightly cooperate to reach and keep the formation, while avoiding collision. This paper proposes a novel decentralized hybrid supervisory control approach for the formation control of multiple UAVs. This is achieved by developing a symbolic motion planning technique to polarly partition the motion space resulting in a finite state discrete event model for the motion dynamics of each UAV. Then, a modular discrete supervisor is designed for different components of the formation mission including reaching the formation, keeping the formation, and collision avoidance. Further, for the collision avoidance mechanism, a novel top-down decomposition-based approach is developed to design local supervisors decentralizedly. It is formally proved that with the proposed top-down decomposition-based approach, the (locally) supervised UAVs, as a whole, can cooperatively satisfy the desired (global) collision avoidance specification. The proposed decentralized supervisory control algorithm is also verified through a hardware-in-the-loop simulator for the formation control of unmanned helicopters.

show abstract

Hierarchical Design for Position-Based Formation Control of Rotorcraft-Like Aerial Vehicles

Cited by 29 publications

References 43 publications

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries

2020 Index IEEE Transactions on Control of Network Systems Vol. 7

Decentralized modular hybrid supervisory control for the formation of unmanned helicopters

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