1,414 research outputs found

    A survey on fractional order control techniques for unmanned aerial and ground vehicles

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    In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

    The adaptive control system of quadrocopter motion

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    In this paper we present a system for automatic control of a quadrocopter based on the adaptive control system. The task is to ensure the motion of the quadrocopter along the given route and to control the stabilization of the quadrocopter in the air in a horizontal or in a given angular position by sending control signals to the engines. The nonlinear model of a quadrocopter is expressed in the form of a linear non-stationary system

    The adaptive control system of quadrocopter motion

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    In this paper we present a system for automatic control of a quadrocopter based on the adaptive control system. The task is to ensure the motion of the quadrocopter along the given route and to control the stabilization of the quadrocopter in the air in a horizontal or in a given angular position by sending control signals to the engines. The nonlinear model of a quadrocopter is expressed in the form of a linear non-stationary system

    Dynamic analysis and qft-based robust control design of a coaxial micro-helicopter

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    This paper presents the dynamic behavior of a coaxial micro-helicopter, under Quantitative Feedback Theory (QFT) control. The flight dynamics of autonomous air vehicles (AAVs) with rotating rings is non-linear and complex. Then, it becomes necessary to characterize these non-linearities for each flight configuration, in order to provide these autonomous air vehicles (AAVs) with autonomous flight and navigation capabilities. Then, the nonlinear model is linearized around the operating point using some assumptions. Finally, a robust QFT control law over the coaxial micro-helicopter is applied to meet some specifications. QFT (quantitative feedback theory) is a control law design method that uses frequency domain concepts to meet performance specifications while managing uncertainty. This method is based on the feedback control when the plant is uncertain or when uncertain disturbances are affecting the plant. The QFT design approach involves conventional frequency response loop shaping by manipulating the gain variable with the poles and zeros of the nominal transfer function. The design process is accomplished by using MATLAB environment software

    ๋ฉ€ํ‹ฐ๋กœํ„ฐ ๊ธฐ๋ฐ˜ ๋‹ค๋ชฉ์  ๋น„ํ–‰ ๋กœ๋ด‡ ํ”Œ๋žซํผ์„ ์œ„ํ•œ ๊ฐ•๊ฑด ์ œ์–ด ๋ฐ ์™„์ „๊ตฌ๋™ ๋น„ํ–‰ ๋งค์ปค๋‹ˆ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€,2020. 2. ๊น€ํ˜„์ง„.์˜ค๋Š˜๋‚  ๋ฉ€ํ‹ฐ๋กœํ„ฐ ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋Š” ๋‹จ์ˆœํ•œ ๋น„ํ–‰ ๋ฐ ๊ณต์ค‘ ์˜์ƒ ์ดฌ์˜์šฉ ์žฅ๋น„์˜ ๊ฐœ๋…์„ ๋„˜์–ด ๋น„ํ–‰ ๋งค๋‹ˆํ“ฐ๋ ˆ์ด์…˜, ๊ณต์ค‘ ํ™”๋ฌผ ์šด์†ก ๋ฐ ๊ณต์ค‘ ์„ผ์‹ฑ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ์ž„๋ฌด์— ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ถ”์„ธ์— ๋งž์ถ”์–ด ๋กœ๋ณดํ‹ฑ์Šค ๋ถ„์•ผ์—์„œ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ๋ฌด์ธํ•ญ๊ณต๊ธฐ๋Š” ๋ถ€๊ณผ๋œ ์ž„๋ฌด์— ๋งž์ถ”์–ด ์›ํ•˜๋Š” ์žฅ๋น„ ๋ฐ ์„ผ์„œ๋ฅผ ์ž์œ ๋กœ์ด ํƒ‘์žฌํ•˜๊ณ  ๋น„ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค๋ชฉ์  ๊ณต์ค‘ ๋กœ๋ด‡ ํ”Œ๋žซํผ์œผ๋กœ ์ธ์‹๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์žฌ์˜ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ํ”Œ๋žซํผ์€ ๋Œํ’ ๋“ฑ์˜ ์™ธ๋ž€์— ๋‹ค์†Œ ๊ฐ•๊ฑดํ•˜์ง€ ๋ชปํ•œ ์ œ์–ด์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค. ๋˜ํ•œ, ๋ณ‘์ง„์šด๋™์˜ ์ œ์–ด๋ฅผ ์œ„ํ•ด ๋น„ํ–‰ ์ค‘ ์ง€์†์ ์œผ๋กœ ๋™์ฒด์˜ ์ž์„ธ๋ฅผ ๋ณ€๊ฒฝํ•ด์•ผ ํ•ด ์„ผ์„œ ๋“ฑ ๋™์ฒด์— ๋ถ€์ฐฉ๋œ ํƒ‘์žฌ๋ฌผ์˜ ์ž์„ธ ๋˜ํ•œ ์ง€์†์ ์œผ๋กœ ๋ณ€ํ™”ํ•œ๋‹ค๋Š” ๋‹จ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์œ„์˜ ๋‘ ๊ฐ€์ง€ ๋ฌธ์ œ๋“ค์„ ํ•ด๊ฒฐํ•˜๊ณ ์ž ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์™ธ๋ž€์— ๊ฐ•๊ฑดํ•œ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ์ œ์–ด๊ธฐ๋ฒ•๊ณผ, ๋ณ‘์ง„์šด๋™๊ณผ ์ž์„ธ์šด๋™์„ ๋…๋ฆฝ์ ์œผ๋กœ ์ œ์–ดํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ํ˜•ํƒœ์˜ ์™„์ „๊ตฌ๋™ ๋ฉ€ํ‹ฐ๋กœํ„ฐ ๋น„ํ–‰ ๋งค์ปค๋‹ˆ์ฆ˜์„ ์†Œ๊ฐœํ•œ๋‹ค. ๊ฐ•๊ฑด ์ œ์–ด๊ธฐ๋ฒ•์˜ ๊ฒฝ์šฐ, ๋จผ์ € ์ •ํ™•ํ•œ ๋ณ‘์ง„์šด๋™ ์ œ์–ด๋ฅผ ์œ„ํ•œ ๋ณ‘์ง„ ํž˜ ์ƒ์„ฑ ๊ธฐ๋ฒ•์„ ์†Œ๊ฐœํ•˜๊ณ  ๋’ค์ด์–ด ๋ณ‘์ง„ ํž˜ ์™ธ๋ž€์— ๊ฐ•๊ฑดํ•œ ์ œ์–ด๋ฅผ ์œ„ํ•œ ์™ธ๋ž€๊ด€์ธก๊ธฐ ๊ธฐ๋ฐ˜ ๊ฐ•๊ฑด ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ค๊ณ„ ๋ฐฉ์•ˆ์„ ๋…ผ์˜ํ•œ๋‹ค. ์ œ์–ด๊ธฐ์˜ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„ ์•ˆ์ •์„ฑ์€ mu ์•ˆ์ •์„ฑ ๋ถ„์„ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๊ฒ€์ฆ๋˜๋ฉฐ, mu ์•ˆ์ •์„ฑ ๋ถ„์„์ด ๊ฐ€์ง€๋Š” ์—„๋ฐ€ํ•œ ์•ˆ์ •์„ฑ ๋ถ„์„์˜ ๊ฒฐ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ์Šค๋ชฐ๊ฒŒ์ธ ์ด๋ก  (Small Gain Theorem) ๊ธฐ๋ฐ˜์˜ ์•ˆ์ •์„ฑ ๋ถ„์„ ๊ฒฐ๊ณผ๊ฐ€ ๋™์‹œ์— ์ œ์‹œ ๋ฐ ๋น„๊ต๋œ๋‹ค. ์ตœ์ข…์ ์œผ๋กœ, ๊ฐœ๋ฐœ๋œ ์ œ์–ด๊ธฐ๋ฅผ ๋„์ž…ํ•œ ๋ฉ€ํ‹ฐ๋กœํ„ฐ์˜ 3์ฐจ์› ๋ณ‘์ง„ ๊ฐ€์†๋„ ์ œ์–ด ์„ฑ๋Šฅ ๋ฐ ํž˜ ๋ฒกํ„ฐ์˜ ํ˜•ํƒœ๋กœ ์ธ๊ฐ€๋˜๋Š” ๋ณ‘์ง„ ์šด๋™ ์™ธ๋ž€์— ๋Œ€ํ•œ ๊ทน๋ณต ์„ฑ๋Šฅ์„ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์—ฌ, ์ œ์•ˆ๋œ ์ œ์–ด๊ธฐ๋ฒ•์˜ ํšจ๊ณผ์ ์ธ ๋น„ํ–‰ ์ง€์  ๋ฐ ๊ถค์  ์ถ”์ข… ๋Šฅ๋ ฅ์„ ํ™•์ธํ•œ๋‹ค. ์™„์ „ ๊ตฌ๋™ ๋ฉ€ํ‹ฐ๋กœํ„ฐ์˜ ๊ฒฝ์šฐ, ๊ธฐ์กด์˜ ์™„์ „๊ตฌ๋™ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๊ฐ€ ๊ฐ€์ง„ ๊ณผ๋„ํ•œ ์ค‘๋Ÿ‰ ์ฆ๊ฐ€ ๋ฐ ์ €์กฐํ•œ ์—๋„ˆ์ง€ ํšจ์œจ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋งค์ปค๋‹ˆ์ฆ˜์„ ์†Œ๊ฐœํ•œ๋‹ค. ์ƒˆ๋กœ์šด ๋งค์ปค๋‹ˆ์ฆ˜์€ ๊ธฐ์กด ๋ฉ€ํ‹ฐ๋กœํ„ฐ์™€ ์ตœ๋Œ€ํ•œ ์œ ์‚ฌํ•œ ํ˜•ํƒœ๋ฅผ ๊ฐ€์ง€๋˜ ์™„์ „๊ตฌ๋™์„ ์œ„ํ•ด ์˜ค์ง ๋‘ ๊ฐœ์˜ ์„œ๋ณด๋ชจํ„ฐ๋งŒ์„ ํฌํ•จํ•˜๋ฉฐ, ์ด๋กœ ์ธํ•ด ๊ธฐ์กด ๋ฉ€ํ‹ฐ๋กœํ„ฐ์™€ ๋น„๊ตํ•ด ์ตœ์†Œํ•œ์˜ ํ˜•ํƒœ์˜ ๋ณ€ํ˜•๋งŒ์„ ๊ฐ€์ง€๋„๋ก ์„ค๊ณ„๋œ๋‹ค. ์ƒˆ๋กœ์šด ํ”Œ๋žซํผ์˜ ๋™์  ํŠน์„ฑ์— ๋Œ€ํ•œ ๋ถ„์„๊ณผ ํ•จ๊ป˜ ์œ ๋„๋œ ์šด๋™๋ฐฉ์ •์‹์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ 6์ž์œ ๋„ ๋น„ํ–‰ ์ œ์–ด๊ธฐ๋ฒ•์ด ์†Œ๊ฐœ๋˜๋ฉฐ, ์ตœ์ข…์ ์œผ๋กœ ๋‹ค์–‘ํ•œ ์‹คํ—˜๊ณผ ๊ทธ ๊ฒฐ๊ณผ๋“ค์„ ํ†ตํ•ด ํ”Œ๋žซํผ์˜ ์™„์ „๊ตฌ๋™ ๋น„ํ–‰ ๋Šฅ๋ ฅ์„ ๊ฒ€์ฆํ•œ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์™„์ „๊ตฌ๋™ ๋ฉ€ํ‹ฐ๋กœํ„ฐ๊ฐ€ ๊ฐ€์ง€๋Š” ์—ฌ๋ถ„์˜ ์ œ์–ด์ž…๋ ฅ(redundancy)๋ฅผ ํ™œ์šฉํ•œ ์ฟผ๋“œ์ฝฅํ„ฐ์˜ ๋‹จ์ผ๋ชจํ„ฐ ๊ณ ์žฅ ๋Œ€๋น„ ๋น„์ƒ ๋น„ํ–‰ ๊ธฐ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ๋น„์ƒ ๋น„ํ–‰ ์ „๋žต์— ๋Œ€ํ•œ ์ž์„ธํ•œ ์†Œ๊ฐœ ๋ฐ ์‹คํ˜„ ๋ฐฉ๋ฒ•, ๋น„์ƒ ๋น„ํ–‰ ์‹œ์˜ ๋™์—ญํ•™์  ํŠน์„ฑ์— ๋Œ€ํ•œ ๋ถ„์„ ๊ฒฐ๊ณผ๊ฐ€ ์†Œ๊ฐœ๋˜๋ฉฐ, ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•œ๋‹ค.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
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