1 research outputs found
λ©ν°λ‘ν° κΈ°λ° λ€λͺ©μ λΉν λ‘λ΄ νλ«νΌμ μν κ°κ±΄ μ μ΄ λ° μμ ꡬλ λΉν 맀컀λμ¦
νμλ
Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν κΈ°κ³ν곡곡νλΆ,2020. 2. κΉνμ§.μ€λλ λ©ν°λ‘ν° λ¬΄μΈν곡기λ λ¨μν λΉν λ° κ³΅μ€ μμ 촬μμ© μ₯λΉμ κ°λ
μ λμ΄ λΉν 맀λν°λ μ΄μ
, κ³΅μ€ νλ¬Ό μ΄μ‘ λ° κ³΅μ€ μΌμ± λ±μ λ€μν μ무μ νμ©λκ³ μλ€. μ΄λ¬ν μΆμΈμ λ§μΆμ΄ λ‘보ν±μ€ λΆμΌμμ λ©ν°λ‘ν° λ¬΄μΈν곡기λ λΆκ³Όλ μ무μ λ§μΆμ΄ μνλ μ₯λΉ λ° μΌμλ₯Ό μμ λ‘μ΄ νμ¬νκ³ λΉνν μ μλ λ€λͺ©μ κ³΅μ€ λ‘λ΄ νλ«νΌμΌλ‘ μΈμλκ³ μλ€.
κ·Έλ¬λ νμ¬μ λ©ν°λ‘ν° νλ«νΌμ λν λ±μ μΈλμ λ€μ κ°κ±΄νμ§ λͺ»ν μ μ΄μ±λ₯μ 보μΈλ€. λν, λ³μ§μ΄λμ μ μ΄λ₯Ό μν΄ λΉν μ€ μ§μμ μΌλ‘ λ체μ μμΈλ₯Ό λ³κ²½ν΄μΌ ν΄ μΌμ λ± λ체μ λΆμ°©λ νμ¬λ¬Όμ μμΈ λν μ§μμ μΌλ‘ λ³ννλ€λ λ¨μ μ κ°μ§κ³ μλ€. μμ λ κ°μ§ λ¬Έμ λ€μ ν΄κ²°νκ³ μ λ³Έ μ°κ΅¬μμλ μΈλμ κ°κ±΄ν λ©ν°λ‘ν° μ μ΄κΈ°λ²κ³Ό, λ³μ§μ΄λκ³Ό μμΈμ΄λμ λ
립μ μΌλ‘ μ μ΄ν μ μλ μλ‘μ΄ ννμ μμ ꡬλ λ©ν°λ‘ν° λΉν 맀컀λμ¦μ μκ°νλ€.
κ°κ±΄ μ μ΄κΈ°λ²μ κ²½μ°, λ¨Όμ μ νν λ³μ§μ΄λ μ μ΄λ₯Ό μν λ³μ§ ν μμ± κΈ°λ²μ μκ°νκ³ λ€μ΄μ΄ λ³μ§ ν μΈλμ κ°κ±΄ν μ μ΄λ₯Ό μν μΈλκ΄μΈ‘κΈ° κΈ°λ° κ°κ±΄ μ μ΄ μκ³ λ¦¬μ¦μ μ€κ³ λ°©μμ λ
Όμνλ€. μ μ΄κΈ°μ νΌλλ°± 루ν μμ μ±μ mu μμ μ± λΆμ κΈ°λ²μ ν΅ν΄ κ²μ¦λλ©°, mu μμ μ± λΆμμ΄ κ°μ§λ μλ°ν μμ μ± λΆμμ κ²°κ³Όλ₯Ό κ²μ¦νκΈ° μν΄ μ€λͺ°κ²μΈ μ΄λ‘ (Small Gain Theorem) κΈ°λ°μ μμ μ± λΆμ κ²°κ³Όκ° λμμ μ μ λ° λΉκ΅λλ€. μ΅μ’
μ μΌλ‘, κ°λ°λ μ μ΄κΈ°λ₯Ό λμ
ν λ©ν°λ‘ν°μ 3μ°¨μ λ³μ§ κ°μλ μ μ΄ μ±λ₯ λ° ν 벑ν°μ ννλ‘ μΈκ°λλ λ³μ§ μ΄λ μΈλμ λν 극볡 μ±λ₯μ μ€νμ ν΅ν΄ κ²μ¦νμ¬, μ μλ μ μ΄κΈ°λ²μ ν¨κ³Όμ μΈ λΉν μ§μ λ° κΆ€μ μΆμ’
λ₯λ ₯μ νμΈνλ€.
μμ ꡬλ λ©ν°λ‘ν°μ κ²½μ°, κΈ°μ‘΄μ μμ ꡬλ λ©ν°λ‘ν°κ° κ°μ§ κ³Όλν μ€λ μ¦κ° λ° μ μ‘°ν μλμ§ ν¨μ¨μ 극볡νκΈ° μν μλ‘μ΄ λ§€μ»€λμ¦μ μκ°νλ€. μλ‘μ΄ λ§€μ»€λμ¦μ κΈ°μ‘΄ λ©ν°λ‘ν°μ μ΅λν μ μ¬ν ννλ₯Ό κ°μ§λ μμ ꡬλμ μν΄ μ€μ§ λ κ°μ μ보λͺ¨ν°λ§μ ν¬ν¨νλ©°, μ΄λ‘ μΈν΄ κΈ°μ‘΄ λ©ν°λ‘ν°μ λΉκ΅ν΄ μ΅μνμ ννμ λ³νλ§μ κ°μ§λλ‘ μ€κ³λλ€. μλ‘μ΄ νλ«νΌμ λμ νΉμ±μ λν λΆμκ³Ό ν¨κ» μ λλ μ΄λλ°©μ μμ κΈ°λ°μΌλ‘ ν 6μμ λ λΉν μ μ΄κΈ°λ²μ΄ μκ°λλ©°, μ΅μ’
μ μΌλ‘ λ€μν μ€νκ³Ό κ·Έ κ²°κ³Όλ€μ ν΅ν΄ νλ«νΌμ μμ ꡬλ λΉν λ₯λ ₯μ κ²μ¦νλ€.
μΆκ°μ μΌλ‘ λ³Έ λ
Όλ¬Έμμλ μμ ꡬλ λ©ν°λ‘ν°κ° κ°μ§λ μ¬λΆμ μ μ΄μ
λ ₯(redundancy)λ₯Ό νμ©ν μΏΌλμ½₯ν°μ λ¨μΌλͺ¨ν° κ³ μ₯ λλΉ λΉμ λΉν κΈ°λ²μ μκ°νλ€. λΉμ λΉν μ λ΅μ λν μμΈν μκ° λ° μ€ν λ°©λ², λΉμ λΉν μμ λμνμ νΉμ±μ λν λΆμ κ²°κ³Όκ° μκ°λλ©°, μ€νκ²°κ³Όλ₯Ό ν΅ν΄ μ μλ κΈ°λ²μ νλΉμ±μ κ²μ¦νλ€.Recently, multi-rotor unmanned aerial vehicles (UAVs) are used for a variety of missions beyond its basic flight, including aerial manipulation, aerial payload transportation, and aerial sensor platform. Following this trend, the multirotor UAV is recognized as a versatile aerial robotics platform that can freely mount and fly the necessary mission equipment and sensors to perform missions.
However, the current multi-rotor platform has a relatively poor ability to maintain nominal flight performance against external disturbances such as wind or gust compared to other robotics platforms. Also, the multirotor suffers from maintaining a stable payload attitude, due to the fact that the attitude of the fuselage should continuously be changed for translational motion control. Particularly, unstabilized fuselage attitude can be a drawback for multirotor's mission performance in such cases as like visual odometry-based flight, since the fuselage-attached sensor should also be tilted during the flight and therefore causes poor sensor information acquisition.
To overcome the above two problems, in this dissertation, we introduce a robust multirotor control method and a novel full-actuation mechanism which widens the usability of the multirotor. The goal of the proposed control method is to bring robustness to the translational motion control against various weather conditions.
And the goal of the full actuation mechanism is to allow the multi-rotor to take arbitrary payload/fuselage attitude independently of the translational motion.
For robust multirotor control, we first introduce a translational force generation technique for accurate translational motion control and then discuss the design method of disturbance observer (DOB)-based robust control algorithm. The stability of the proposed feedback controller is validated by the mu-stability analysis technique, and the results are compared to the small-gain theorem (SGT)-based stability analysis to validate the rigorousness of the analysis. Through the experiments, we validate the translational acceleration control performance of the developed controller and confirm the robustness against external disturbance forces.
For a fully-actuated multirotor platform, we propose a new mechanism called a T3-Multirotor that can overcome the excessive weight increase and poor energy efficiency of the existing fully-actuated multirotor.
The structure of the new platform is designed to be as close as possible to the existing multi-rotor and includes only two servo motors for full actuation. The dynamic characteristics of the new platform are analyzed and a six-degree-of-freedom (DOF) flight controller is designed based on the derived equations of motion. The full actuation of the proposed platform is then validated through various experiments.
As a derivative study, this paper also introduces an emergency flight technique to prepare for a single motor failure scenario of a multi-rotor using the redundancy of the T3-Multirotor platform. The detailed introduction and implementation method of the emergency flight strategy with the analysis of the dynamic characteristics during the emergency flight is introduced, and the experimental results are provided to verify the validity of the proposed technique.1 Introduction 1
1.1 Motivation 1
1.2 Literature survey 3
1.2.1 Robust translational motion control 3
1.2.2 Fully-actuated multirotor platform 4
1.3 Research objectives and contributions 5
1.3.1 Goal #I: Robust multirotor motion control 5
1.3.2 Goal #II: A new fully actuated multirotor platform 6
1.3.3 Goal #II-A: T3-Multirotor-based fail-safe flight 7
1.4 Thesis organization 7
2 Multi-Rotor Unmanned Aerial Vehicle: Overview 9
2.1 Platform overview 9
2.2 Mathematical model of multi-rotor UAV 10
3 Robust Translational Motion Control 13
3.1 Introduction 14
3.2 Translational force/acceleration control 14
3.2.1 Relationship between \mathbf{r} and \tilde{\ddot{\mathbf{X}}} 15
3.2.2 Calculation of \mathbf{r}_d from \tilde{\ddot{\mathbf{X}}}_d considering dynamics 16
3.3 Disturbance observer 22
3.3.1 An overview of the disturbance-merged overall system 22
3.3.2 Disturbance observer 22
3.4 Stability analysis 26
3.4.1 Modeling of P(s) considering uncertainties 27
3.4.2 \tau-determination through \mu-analysis 30
3.5 Simulation and experimental result 34
3.5.1 Validation of acceleration tracking performance 34
3.5.2 Validation of DOB performance 34
4 Fully-Actuated Multirotor Mechanism 39
4.1 Introduction 39
4.2 Mechanism 40
4.3 Modeling 42
4.3.1 General equations of motion of TP and FP 42
4.3.2 Simplified equations of motion of TP and FP 46
4.4 Controller design 49
4.4.1 Controller overview 49
4.4.2 Independent roll and pitch attitude control of TP and FP 50
4.4.3 Heading angle control 54
4.4.4 Overall control scheme 54
4.5 Simulation result 56
4.5.1 Scenario 1: Changing FP attitude during hovering 58
4.5.2 Scenario 2: Fixing FP attitude during translation 58
4.6 Experimental result 60
4.6.1 Scenario 1: Changing FP attitude during hovering 60
4.6.2 Scenario 2: Fixing FP attitude during translation 60
4.7 Applications 63
4.7.1 Personal aerial vehicle 63
4.7.2 High MoI payload transportation platform - revisit of [1] 63
4.7.3 Take-off and landing on an oscillating landing pad 64
5 Derived Research: Fail-safe Flight in a Single Motor Failure Scenario 67
5.1 Introduction 67
5.1.1 Related works 68
5.1.2 Contributions 68
5.2 Mechanism and dynamics 69
5.2.1 Mechanism 69
5.2.2 Platform dynamics 70
5.3 Fail-safe flight strategy 75
5.3.1 Fail-safe flight method 75
5.3.2 Hardware condition for single motor fail-safe flight 80
5.4 Controller design 83
5.4.1 Faulty motor detection 83
5.4.2 Controller design 84
5.4.3 Attitude dynamics in fail-safe mode 86
5.5 Experiment result 90
5.5.1 Experimental settings 90
5.5.2 Stability and control performance review 92
5.5.3 Flight results 93
6 Conclusions 96
Abstract (in Korean) 107Docto