Partially Integrated Guidance and Control of Unmanned Aerial Vehicles for Reactive Obstacle Avoidance

2016 ◽  
pp. 357-383
Author(s):  
Radhakant Padhi ◽  
Charu Chawla
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Obstacle Avoidance ◽  
Guidance And Control ◽  
Aerial Vehicles ◽  
Integrated Guidance And Control ◽  
And Control

scholarly journals Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4324
Author(s):  
Salvatore Rosario Bassolillo ◽  
Egidio D’Amato ◽  
Immacolata Notaro ◽  
Luciano Blasi ◽  
Massimiliano Mattei
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Predictive Control ◽  
Control Strategy ◽  
Target Area ◽  
Guidance And Control ◽  
Aerial Vehicles ◽  
Control Scheme ◽  
And Control ◽  
Numerical Simulator

This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft.

scholarly journals Obstacle avoidance and distance measurement for unmanned aerial vehicles using monocular vision

2019 ◽  
Vol 9 (5) ◽  
pp. 3504
Author(s):  
Aswini N ◽  
Uma S V
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Obstacle Avoidance ◽  
Communication Systems ◽  
Scale Invariant ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Complex Automation ◽  
Novel Method ◽  
Scale Invariant Feature ◽  
And Control

<span lang="EN-US">Unmanned Aerial Vehicles or commonly known as drones are better suited for "dull, dirty, or dangerous" missions than manned aircraft. The drone can be either remotely controlled or it can travel as per predefined path using complex automation algorithm built during its development. In general, Unmanned Aerial Vehicle (UAV) is the combination of Drone in the air and control system on the ground. Design of an UAV means integrating hardware, software, sensors, actuators, communication systems and payloads into a single unit for the application involved. To make it completely autonomous, the most challenging problem faced by UAVs is obstacle avoidance. In this paper, a novel method to detect frontal obstacles using monocular camera is proposed. Computer Vision algorithms like Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are used to detect frontal obstacles and then distance of the obstacle from camera is calculated. To meet the defined objectives, designed system is tested with self-developed videos which are captured by DJI Phantom 4 pro.</span>

Flight modeling and simulation for dynamic soaring with small unmanned air vehicles

2016 ◽  
Vol 231 (4) ◽  
pp. 589-605 ◽  
Author(s):  
Duo-Neng Liu ◽  
Zhong-Xi Hou ◽  
Xian-Zhong Gao
Keyword(s):  
Modeling And Simulation ◽  
Unmanned Aerial Vehicles ◽  
Energy Analysis ◽  
Equations Of Motion ◽  
Flight Simulation ◽  
Simulation Platform ◽  
Dynamic Soaring ◽  
Guidance And Control ◽  
Aerial Vehicles ◽  
And Control

The design of guidance and control strategies is a promising study trend of dynamic soaring for small unmanned aerial vehicles, for which the flight modeling and simulation specifically for soaring-capable unmanned aerial vehicles is significant and necessary. The aim of this paper is to propose a flight simulation platform for dynamic soaring. In order to do so, firstly, two different sets of equations of motion of small unmanned aerial vehicles have been derived and their characteristics are compared: one is expressed in body-fixed frame and the other in air-relative flight path frame. Secondly, the latter set is used for energy analysis to maximize energy gain while climbing and diving and minimize energy cost during turning in dynamic soaring. While the former serves to build the dynamic soaring simulation platform, in which a piecewise trajectory-based guidance and control strategy according to the energy analysis is proposed tracking the optimum climb and bank angles and traveling toward desired directions. Simulation results indicate that the unmanned aerial vehicles can perform dynamic soaring toward various directions in different wind fields, follow asymptotically the typical straight-line and circular-orbit paths by repeating soaring cycles.

Integral barrier Lyapunov functions-based integrated guidance and control design for strap-down missile with field-of-view constraint

2021 ◽  
pp. 014233122098132
Author(s):  
Bin Zhao ◽  
Zhenxin Feng ◽  
Jianguo Guo
Keyword(s):  
Sliding Mode ◽  
State Equation ◽  
Field Of View ◽  
Feedback Form ◽  
Theoretical Derivation ◽  
Guidance And Control ◽  
Integrated Guidance And Control ◽  
Twisting Algorithm ◽  
And Control ◽  
Special Time

The problem of the integrated guidance and control (IGC) design for strap-down missile with the field-of-view (FOV) constraint is solved by using the integral barrier Lyapunov function (iBLF) and the sliding mode control theory. Firstly, the nonlinear and uncertainty state equation with non-strict feedback form for IGC design is derived by using the strap-down decoupling strategy. Secondly, a novel adaptive finite time disturbance observer is proposed to estimate the uncertainties based on an improved adaptive gain super twisting algorithm. Thirdly, the special time-varying sliding variable is designed and the iBLF is employed to handle the problem of FOV constraint. Theoretical derivation and simulation show that the IGC system is globally uniformly ultimately bounded and the FOV angle constraint is also guaranteed not only during the reaching phase but also during the sliding mode phase.

Modeling and control of a quadrotor tail-sitter unmanned aerial vehicles

2021 ◽  
pp. 095965182110504
Author(s):  
Hongbo Xin ◽  
Yujie Wang ◽  
Xianzhong Gao ◽  
Qingyang Chen ◽  
Bingjie Zhu ◽  
...  
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Euler Angle ◽  
Level Flight ◽  
Aerial Vehicles ◽  
Flight Mode ◽  
Aerial Vehicle ◽  
Hover Flight ◽  
And Control

The tail-sitter unmanned aerial vehicles have the advantages of multi-rotors and fixed-wing aircrafts, such as vertical takeoff and landing, long endurance and high-speed cruise. These make the tail-sitter unmanned aerial vehicle capable for special tasks in complex environments. In this article, we present the modeling and the control system design for a quadrotor tail-sitter unmanned aerial vehicle whose main structure consists of a traditional quadrotor with four wings fixed on the four rotor arms. The key point of the control system is the transition process between hover flight mode and level flight mode. However, the normal Euler angle representation cannot tackle both of the hover and level flight modes because of the singularity when pitch angle tends to [Formula: see text]. The dual-Euler method using two Euler-angle representations in two body-fixed coordinate frames is presented to couple with this problem, which gives continuous attitude representation throughout the whole flight envelope. The control system is divided into hover and level controllers to adapt to the two different flight modes. The nonlinear dynamic inverse method is employed to realize fuselage rotation and attitude stabilization. In guidance control, the vector field method is used in level flight guidance logic, and the quadrotor guidance method is used in hover flight mode. The framework of the whole system is established by MATLAB and Simulink, and the effectiveness of the guidance and control algorithms are verified by simulation. Finally, the flight test of the prototype shows the feasibility of the whole system.

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