Modelling of Dynamic and Control of Six-Rotor Autonomous Unmanned Aerial Vehicle

2013 ◽  
Vol 198 ◽  
pp. 220-225 ◽  
Author(s):  
Daniel Ołdziej ◽  
Zdzisław Gosiewski
Keyword(s):  
Equations Of Motion ◽  
Control Plant ◽  
Open Loop ◽  
Simulation Environment ◽  
Open Loop System ◽  
Plant Model ◽  
Modeling And Control ◽  
Lqr Controller ◽  
Aerial Vehicle ◽  
And Control

The paper describes matters of modeling and control of aerial vehicle in rotorcraft configuration. Equations of motion were derived and dynamic model of six-rotor was build. To find the best adjustment of model to real object the dynamics of the drives was joined to the control plant model. Trimming and linearization were performed. Open loop system and LQR controller were checked in simulation environment. Next the faults of the actuator/sensor elements were arranged and the minimal number of observed outputs and drives for LQR stabilization process were specified.

Dynamic Modeling and Control Techniques for a Quadrotor

2019 ◽  
pp. 20-66
Author(s):  
Heba Elkholy ◽  
Maki K. Habib
Keyword(s):  
Pid Controller ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Simulation Environment ◽  
Modeling And Control ◽  
Controller Gain ◽  
Aerial Vehicle ◽  
And Control ◽  
The Mathematical Model

This chapter presents the detailed dynamic model of a Vertical Take-Off and Landing (VTOL) type Unmanned Aerial Vehicle (UAV) known as the quadrotor. The mathematical model is derived based on Newton Euler formalism. This is followed by the development of a simulation environment on which the developed model is verified. Four control algorithms are developed to control the quadrotor's degrees of freedom: a linear PID controller, Gain Scheduling-based PID controller, nonlinear Sliding Mode, and Backstepping controllers. The performances of these controllers are compared through the developed simulation environment in terms of their dynamic performance, stability, and the effect of possible disturbances.

Dynamic Modeling and Control Techniques for a Quadrotor

2015 ◽  
pp. 408-454
Author(s):  
Heba Elkholy ◽  
Maki K. Habib
Keyword(s):  
Pid Controller ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Simulation Environment ◽  
Modeling And Control ◽  
Controller Gain ◽  
Aerial Vehicle ◽  
And Control ◽  
The Mathematical Model

This chapter presents the detailed dynamic model of a Vertical Take-Off and Landing (VTOL) type Unmanned Aerial Vehicle (UAV) known as the quadrotor. The mathematical model is derived based on Newton Euler formalism. This is followed by the development of a simulation environment on which the developed model is verified. Four control algorithms are developed to control the quadrotor's degrees of freedom: a linear PID controller, Gain Scheduling-based PID controller, nonlinear Sliding Mode, and Backstepping controllers. The performances of these controllers are compared through the developed simulation environment in terms of their dynamic performance, stability, and the effect of possible disturbances.

Development of Autonomous Quad-Tilt-Wing (QTW) Unmanned Aerial Vehicle: Design, Modeling, and Control

2010 ◽  
pp. 77-93 ◽  
Author(s):  
Kenzo Nonami ◽  
Farid Kendoul ◽  
Satoshi Suzuki ◽  
Wei Wang ◽  
Daisuke Nakazawa
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vehicle Design ◽  
Modeling And Control ◽  
Aerial Vehicle ◽  
And Control

Modeling of Multibody Flexible Articulated Structures With Mutually Coupled Motions: Part II — Application and Results

1992 ◽  
Author(s):  
Jiechi Xu ◽  
Joseph R. Baumgarten
Keyword(s):  
Experimental Data ◽  
Degrees Of Freedom ◽  
Equations Of Motion ◽  
Open Loop ◽  
Body Motion ◽  
Universal Joint ◽  
Open Loop System ◽  
Numerical Examples ◽  
Kinematic Properties ◽  
Good Agreement

Abstract The application of the systematic procedures in the derivation of the equations of motion proposed in Part I of this work is demonstrated and implemented in detail. The equations of motion for each subsystem are derived individually and are assembled under the concept of compatibility between the local kinematic properties of the elastic degrees of freedom of those connected elastic members. The specific structure under consideration is characterized as an open loop system with spherical unconstrained chains being capable of rotating about a Hooke’s or universal joint. The rigid body motion, due to two unknown rotations, and the elastic degrees of freedom are mutually coupled and influence each other. The traditional motion superposition approach is no longer applicable herein. Numerical examples for several cases are presented. These simulations are compared with the experimental data and good agreement is indicated.

Modeling and control of a quadrotor unmanned aerial vehicle using type-2 fuzzy systems

2021 ◽  
pp. 25-46
Author(s):  
Ayad Al-Mahturi ◽  
Fendy Santoso ◽  
Matthew A. Garratt ◽  
Sreenatha G. Anavatti
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Fuzzy Systems ◽  
Modeling And Control ◽  
Aerial Vehicle ◽  
And Control

Survey on Aerial Manipulator: System, Modeling, and Control

Robotica ◽  
2019 ◽  
Vol 38 (7) ◽  
pp. 1288-1317 ◽  
Author(s):  
Xiangdong Meng ◽  
Yuqing He ◽  
Jianda Han
Keyword(s):  
System Modeling ◽  
Future Research ◽  
Typical Application ◽  
Modeling And Control ◽  
Aerial Manipulator ◽  
Initial Appearance ◽  
Aerial Vehicle ◽  
New Type ◽  
And Control ◽  
Flying Robot

SUMMARYThe aerial manipulator is a special and new type of flying robot composed of a rotorcraft unmanned aerial vehicle (UAV) and a/several manipulator/s. It has gained a lot of attention since its initial appearance in 2010. This is mainly because it enables traditional UAVs to conduct versatile manipulating tasks from air, considerably enriching their applications. In this survey, a complete and systematic review of related research on this topic is conducted. First, various types of structure designs of aerial manipulators are listed out. Subsequently, the modeling and control methods are introduced in detail from the perspective of two types of typical application cases: free-flight and motion-restricted operations. Finally, challenges for future research are presented.

Modeling and Control of a Rotary Crane

1985 ◽  
Vol 107 (3) ◽  
pp. 200-206 ◽  
Author(s):  
Y. Sakawa ◽  
A. Nakazumi
Keyword(s):  
Feedback Control ◽  
Equilibrium State ◽  
Open Loop ◽  
The State ◽  
Control Input ◽  
Loop Control ◽  
Modeling And Control ◽  
Control Scheme ◽  
Rotary Crane ◽  
And Control

In this paper we first derive a dynamical model for the control of a rotary crane, which makes three kinds of motion (rotation, load hoisting, and boom hoisting) simultaneously. The goal is to transfer a load to a desired place in such a way that at the end of transfer the swing of the load decays as quickly as possible. We first apply an open-loop control input to the system such that the state of the system can be transferred to a neighborhood of the equilibrium state. Then we apply a feedback control signal so that the state of the system approaches the equilibrium state as quickly as possible. The results of computer simulation prove that the open-loop plus feedback control scheme works well.

High-Level Modeling and Control of the Bebop 2 Micro Aerial Vehicle

2020 ◽  
Author(s):  
Anthony Oliveira Pinto ◽  
Harrison Neves Marciano ◽  
Vinicius Pacheco Bacheti ◽  
Mauro Sergio Mafra Moreira ◽  
Alexandre Santos Brandao ◽  
...  
Keyword(s):  
Micro Aerial Vehicle ◽  
Modeling And Control ◽  
Aerial Vehicle ◽  
High Level Modeling ◽  
High Level ◽  
And Control

Design Optimization, Modeling, and Control of Unmanned Aerial Vehicle Lifted By Coandă Effect

2017 ◽  
Vol 22 (3) ◽  
pp. 1327-1336 ◽  
Author(s):  
Hyunyong Lee ◽  
Seonhye Han ◽  
Hyoju Lee ◽  
Jaehyeok Jeon ◽  
Choonghan Lee ◽  
...  
Keyword(s):  
Design Optimization ◽  
Unmanned Aerial Vehicle ◽  
Coanda Effect ◽  
Optimization Modeling ◽  
Modeling And Control ◽  
Aerial Vehicle ◽  
Coandǎ Effect ◽  
And Control

Modeling and control through leadership of a refined flocking system

2014 ◽  
Vol 25 (02) ◽  
pp. 255-282 ◽  
Author(s):  
Alfio Borzì ◽  
Suttida Wongkaew
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Social Systems ◽  
Open Loop ◽  
Modeling And Control ◽  
Control Scheme ◽  
Nonlinear Conjugate Gradient ◽  
Optimality Systems ◽  
And Control ◽  
Conjugate Gradient Solver

A new refined flocking model that includes self-propelling, friction, attraction and repulsion, and alignment features is presented. This model takes into account various behavioral phenomena observed in biological and social systems. In addition, the presence of a leader is included in the system in order to develop a control strategy for the flocking model to accomplish desired objectives. Specifically, a model predictive control scheme is proposed that requires the solution of a sequence of open-loop optimality systems. An accurate Runge–Kutta scheme to discretize the optimality systems and a nonlinear conjugate gradient solver are implemented and discussed. Numerical experiments are performed that investigate the properties of the refined flocking model and demonstrate the ability of the control strategy to drive the flocking system to attain a desired target configuration and to follow a given trajectory.

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