Elastic Formation Keeping Control of Unmanned Aerial Vehicle Adapting to Flight Speed

2013 ◽  
Vol 367 ◽  
pp. 411-416 ◽  
Author(s):  
Guang Yan Xu ◽  
Yi Bo Shi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Relative Position ◽  
State Feedback ◽  
Feedback Controller ◽  
Flight Speed ◽  
Dynamic Equations ◽  
Aerial Vehicle ◽  
Distance Vector ◽  
Simulation Results ◽  
Formation Keeping

For an Unmanned Aerial Vehicle (UAV) formation in leader-follower mode, considering the relative position relationship between neighbor vehicles in the formation, an elastic distance vector is proposed. The dynamic equations of a flight speed adaptive UAV formation are established using the elastic distance vector we proposed. The state feedback controller is designed. Simulation results show that the controller can be used to control the follower vehicles to follow the leader vehicle maneuvering effectively and keep the desired formation well, most importantly, the relative distance between neighbor vehicles in the formation is adapted to the changes of flight speed.

State Feedback Controller - An Alternative Approach Applied to Unmanned Aerial Vehicle

2014 ◽  
Vol 704 ◽  
pp. 270-276
Author(s):  
Renato A. Aguiar ◽  
Fabrizio Leonardi
Keyword(s):  
Control Systems ◽  
Unmanned Aerial Vehicle ◽  
State Feedback ◽  
The State ◽  
Feedback Controller ◽  
Feedback Gain ◽  
State Feedback Controller ◽  
Alternative Approach ◽  
Aerial Vehicle ◽  
Alternative Methodology

The primary goal of this work is to propose an alternative methodology as a first approach in the design of control systems by means of a feedback state gain. The proposed method is detailed and an application is presented. The results show relevant aspects regarding the state feedback gain, especially in regard to variation in the parameters of the plant.

scholarly journals PERANCANGAN AUTOPILOT LATERAL-DIREKSIONAL PESAWAT NIRAWAK LSU-05 (THE DESIGN OF THE LATERAL-DIRECTIONAL AUTOPILOT FOR THE LSU-05 UNMANNED AERIAL VEHICLE)

2018 ◽  
Vol 15 (2) ◽  
pp. 93 ◽  
Author(s):  
Muhammad Fajar ◽  
Ony Arifianto
Keyword(s):  
State Space ◽  
Linear Model ◽  
Unmanned Aerial Vehicle ◽  
Pid Controller ◽  
Settling Time ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Directional Motion ◽  
Aerial Vehicle ◽  
Simulation Results

The autopilot on the aircraft is developed based on the mode of motion of the aircraft i.e. longitudinal and lateral-directional motion. In this paper, an autopilot is designed in lateral-directional mode for LSU-05 aircraft. The autopilot is designed at a range of aircraft operating speeds of 15 m/s, 20 m/s, 25 m/s, and 30 m/s at 1000 m altitude. Designed autopilots are Roll Attitude Hold, Heading Hold and Waypoint Following. Autopilot is designed based on linear model in the form of state-space. The controller used is a Proportional-Integral-Derivative (PID) controller. Simulation results show the value of overshoot / undershoot does not exceed 5% and settling time is less than 30 second if given step command. Abstrak Autopilot pada pesawat dikembangkan berdasarkan pada modus gerak pesawat yaitu modus gerak longitudinal dan lateral-directional. Pada makalah ini, dirancang autopilot pada modus gerak lateral-directional untuk pesawat LSU-05. Autopilot dirancang pada range kecepatan operasi pesawat yaitu 15 m/dtk, 20 m/dtk, 25 m/dtk, dan 30 m/dtk dengan ketinggian 1000 m. Autopilot yang dirancang adalah Roll Attitude Hold, Heading Hold dan Waypoint Following. Autopilot dirancang berdasarkan model linier dalam bentuk state-space. Pengendali yang digunakan adalah pengendali Proportional-Integral-Derivative (PID). Hasil simulasi menunjukan nilai overshoot/undershoot tidak melebihi 5% dan settling time kurang dari 30 detik jika diberikan perintah step.

Swarming Algorithm for Unmanned Aerial Vehicle (UAV) Quadrotors – Swarm Behavior for Aggregation, Foraging, Formation, and Tracking –

2014 ◽  
Vol 18 (5) ◽  
pp. 745-751 ◽  
Author(s):  
Argel A. Bandala ◽  
◽  
Elmer P. Dadios ◽  
Ryan Rhay P. Vicerra ◽  
Laurence A. Gan Lim
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Computer Simulations ◽  
Group Performance ◽  
Robotic System ◽  
Robot Swarms ◽  
Average Accuracy ◽  
Swarm Behavior ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Robotic Agent

This paper presents the fusion of swarm behavior in multi robotic system specifically the quadrotors unmanned aerial vehicle (QUAV) operations. This study directed on using robot swarms because of its key feature of decentralized processing amongst its member. This characteristic leads to advantages of robot operations because an individual robot failure will not affect the group performance. The algorithm emulating the animal or insect swarm behaviors is presented in this paper and implemented into an artificial robotic agent (QUAV) in computer simulations. The simulation results concluded that for increasing number of QUAV the aggregation accuracy increases with an accuracy of 90.62%. The experiment for foraging revealed that the number of QUAV does not affect the accuracy of the swarm instead the iterations needed are greatly improved with an average of 160.53 iterations from 50 to 500 QUAV. For swarm tracking, the average accuracy is 89.23%. The accuracy of the swarm formation is 84.65%. These results clearly defined that the swarm system is accurate enough to perform the tasks and robust in any QUAV number.

scholarly journals Intra-layer Synchronization in a Duplex Networks with Noise

2021 ◽  
Author(s):  
Jinsen Zhuang ◽  
Yan Zhou ◽  
Yonghui Xia
Keyword(s):  
Dynamical System ◽  
State Feedback ◽  
Feedback Controller ◽  
The Other ◽  
Time Varying ◽  
Stochastic Perturbations ◽  
State Feedback Controller ◽  
Control Schemes ◽  
Simulation Results ◽  
The Impact

This paper concerns the impact of stochastic perturbations on the intra-layer synchronization of the duplex networks. A duplex network contains two layers ([1,2]). Different from the previous works, environmental noise is introduced into the dynamical system of the duplex network. We incorporate both the inter-layer delay and the intra-layer delay into the dynamical system. Both of the delays are time-varying. However, the paper [1] only considered the intra-layer delays and they are assumed as the constants. While the paper [2] did not consider the inter-layer delay or intra-layer delay. When the system does not achieve automatic intra-layer synchronization, we introduce two controllers: one is the state-feedback controller, the other is the adaptive state-feedback controller. Interestingly, we find that the intra-layer synchronization will achieve automatically if the inter-layer coupling strength $c_1$ is large enough when the time-varying inter-layer delays are absent. Finally, some interesting simulation results are obtained for the Chua-Chua chaotic system with application of our theoretic results, which show the feasibility effectiveness of our control schemes.

Dynamic Modeling for a Miniature Six-Rotor Unmanned Aerial Vehicle

2013 ◽  
Vol 321-324 ◽  
pp. 819-823 ◽  
Author(s):  
Qi Dong Ma ◽  
Zhen Guo Sun ◽  
Jing Ran Wu ◽  
Wen Zeng Zhang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Driving Force ◽  
Pid Controller ◽  
Control Algorithm ◽  
Driving Forces ◽  
Euler Angles ◽  
Unstable System ◽  
Nonlinear Dynamic Model ◽  
Aerial Vehicle ◽  
Simulation Results

A nonlinear dynamic model of a miniature Six-Rotor is presented. A 4 channels PID controller is designed to operate the under actuated and dynamically unstable system with 6 inputs. Driving forces of 6 rotors are divided into four components such as throttle, roll, pitch and yaw. The control algorithm is simulated with Design Optimization Toolbox in Matlab. After observing the corresponding responses of Euler angles, the altitude and the driving force for each motor, the simulation results show good performance.

Takeoff Analysis and Simulation of a Small Scaled UAV with a Rocket Booster

2011 ◽  
Vol 267 ◽  
pp. 674-682 ◽  
Author(s):  
Bo Liu ◽  
Zhou Fang ◽  
Ping Li ◽  
Chuan Chuan Hao
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Unmanned Aerial Vehicle ◽  
Nonlinear Dynamic ◽  
Total Mass ◽  
Nonlinear Dynamic Model ◽  
Centre Of Gravity ◽  
Acceptable Range ◽  
Aerial Vehicle ◽  
Simulation Results

This paper analyses the takeoff process of a small scaled UAV (unmanned aerial vehicle) with a single rocket booster. Because the thrust provided by the rocket booster is 10 times as large as the thrust provided by the engines, the effects caused by the boosting rocket on total mass, compound centre of gravity and inertia can not be neglected and are all considered. The inertia of the boosting rocket is calculated by the means of finite element method. Based on the analysis, a nonlinear dynamic model of the UAV is built. Several simulations with different takeoff parameters are conducted to test the takeoff performance. By analyzing simulation results, the acceptable range of boosting angle is investigated.

scholarly journals Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

2021 ◽  
Author(s):  
Zhi Liu ◽  
Cheng Zhan ◽  
Ying Cui ◽  
Celimuge Wu ◽  
Han Hu
Keyword(s):  
Video Streaming ◽  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Aerial Vehicle ◽  
Cooperative Computing ◽  
Computing Performance ◽  
Simulation Results

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

scholarly journals Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

2021 ◽  
Author(s):  
Zhi Liu ◽  
Cheng Zhan ◽  
Ying Cui ◽  
Celimuge Wu ◽  
Han Hu
Keyword(s):  
Video Streaming ◽  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Aerial Vehicle ◽  
Cooperative Computing ◽  
Computing Performance ◽  
Simulation Results

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

Sign in / Sign up

Export Citation Format

Share Document