Time-varying formation dynamics modeling and constrained trajectory optimization of multi-quadrotor UAVs

Li, Xi; Qi, Guoyuan; Zhang, Limin

doi:10.1007/s11071-021-06788-3

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…As this paper primarily focuses on studying the translational motion of a quadrotor UAV, it is assumed that an attitude controller exists to regulate its roll, pitch, and yaw angles. With the incorporation of References 6 and 8, the translational dynamics model for the

i

th quadrotor UAV can be formulated as follows.

$$ \left\{\begin{array}{c}{\ddot{x}}_i={u}_{1i}\left(c{\phi}_is{\theta}_ic{\psi}_i+s{\phi}_is{\psi}_i\right)/{m}_i-{k}_{1i}{\dot{x}}_i/{m}_i+{d}_{xi}\\ {}{...

…”

Section: Preliminaries and System Formulationmentioning

confidence: 99%

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Learning‐based collision avoidance and robust H_∞ optimal formation control for uncertain quadrotor UAV systems

Li,

Wang,

Luo

et al. 2023

Intl J Robust & Nonlinear

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This paper investigates the robust optimal formation control for the position subsystem of quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances and collision constraints. To prevent collision with both members of the formation and external obstacles, a collision avoidance potential function is constructed using relative position and velocity information. The basic bounded control input can ensure the stable flight and collision avoidance of a quadrotor UAV system. Based on the approximate dynamic programming (ADP) framework and two‐player zero‐sum differential game theory, the optimal controller is designed to further enhance the control performance of the system. The optimal value function is approximated by a single layer neural network, which avoids solving complex nonlinear Hamilton‐JacobiIsaac (HJI) equation. The stability of the closed loop system is proved. The effectiveness of the robust optimal formation controller based on learning is validated through MATLAB simulation in two distinct scenarios.

show abstract

i

th quadrotor UAV can be formulated as follows.

$$ \left\{\begin{array}{c}{\ddot{x}}_i={u}_{1i}\left(c{\phi}_is{\theta}_ic{\psi}_i+s{\phi}_is{\psi}_i\right)/{m}_i-{k}_{1i}{\dot{x}}_i/{m}_i+{d}_{xi}\\ {}{...

…”

Section: Preliminaries and System Formulationmentioning

confidence: 99%

“…For instance, quadrotor UAV formation control requires high precision, 6 high speed, and high sensitivity 7 . Depending on the specific task, such as formation reconstruction, 8 task reallocation, 9 and moving target tracking, 10 the performance requirements of the formation will vary.…”

Section: Introductionmentioning

confidence: 99%

Learning‐based collision avoidance and robust H_∞ optimal formation control for uncertain quadrotor UAV systems

Li,

Wang,

Luo

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Therefore, the control process should ensure that its flight altitude is always higher than the safety altitude; otherwise, it may make the QUAV crash. However, the current literature on the flight control of QUAVs hardly considers it, except for [18], which designs a trajectory optimization algorithm based on the Gaussian pseudospectral method of altitude that gives an altitude constraint. However, it should be noted that this work is not fixed time, which means that the convergence time has uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

A Switching Mode Control Scheme for the Hovering Control of Quadrotor Unmanned Aerial Vehicles

Cheng

Wang

2023

Mathematics

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This paper presents a novel switching mode control scheme for the six-DOF hovering control of underactuated quadrotor unmanned aerial vehicles (QUAVs) with strong coupling. Through this paper, the full six states of the position and attitude of the QUAV can be controlled to the special target configuration in a fixed time. First, a continuously differentiable fixed time controller with a state constraint was designed for the position system. Second, a fixed-time integral sliding mode controller was designed for the attitude subsystem. Thirdly, a switching law was designed to switch the above two types of controllers a limited number of times during hovering control. Additionally, the crash problem is fully discussed during the entire control process. In summary, the full-state hover mission was completed. The simulation experiments verify the effectiveness of the control algorithm.

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“…Inspired by the Voronoi diagram, the formation control methods based on it have the advantages of flexibility and strong robustness, as well as inherent collision avoidance capabilities between drones [16,17]. By utilizing this approach, the Voronoi cells can be used as safe and collision-free regions for agent motion planning and collision avoidance among themselves [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Omni-Directional Capture for Multi-Drone Based on 3D-Voronoi Tessellation

Cao

Chen

Gao

et al. 2023

Drones

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This paper addresses the multi-drone formation capture in three-dimensional (3D) environments. The omni-directional minimum volume (ODMV) 3D-Voronoi diagram algorithm is proposed for the first time to achieve the two goals of (1) forming and keeping a capture and (2) planning the control action within its safe, collision region for each drone. First, we extend the traditional 2D Voronoi diagram to the 3D environment and use the non-overlapping spatial division property of 3D Voronoi diagram to inherently avoid the collision between drones. Second, we make improvements to the problem of capture angle in our minimum area strategy and propose an omni-directional minimum volume strategy to accomplish the effective capture of a target by constraining the capture angle. Finally, the wolf pack algorithm (WPA) with variable step size is introduced to provide a movement strategy for multi-drone formations. Thus, the proposed ODMV can also achieve dynamic target and multi target capture in environments with obstacles. The Optitrack motion capture system and Crazyflie drones are used to conduct the multi-drone capture experiment. Both simulation and experimental results are included to demonstrated the effectiveness of the proposed ODMV method.

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Time-varying formation dynamics modeling and constrained trajectory optimization of multi-quadrotor UAVs

Cited by 13 publications

References 35 publications

Learning‐based collision avoidance and robust H_∞ optimal formation control for uncertain quadrotor UAV systems

Learning‐based collision avoidance and robust H_∞ optimal formation control for uncertain quadrotor UAV systems

A Switching Mode Control Scheme for the Hovering Control of Quadrotor Unmanned Aerial Vehicles

Omni-Directional Capture for Multi-Drone Based on 3D-Voronoi Tessellation

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Time-varying formation dynamics modeling and constrained trajectory optimization of multi-quadrotor UAVs

Cited by 13 publications

References 35 publications

Learning‐based collision avoidance and robust H∞ optimal formation control for uncertain quadrotor UAV systems

Learning‐based collision avoidance and robust H∞ optimal formation control for uncertain quadrotor UAV systems

A Switching Mode Control Scheme for the Hovering Control of Quadrotor Unmanned Aerial Vehicles

Omni-Directional Capture for Multi-Drone Based on 3D-Voronoi Tessellation

Contact Info

Product

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Learning‐based collision avoidance and robust H_∞ optimal formation control for uncertain quadrotor UAV systems

Learning‐based collision avoidance and robust H_∞ optimal formation control for uncertain quadrotor UAV systems