333 research outputs found

    Lyapunov-based fault tolerant control of quadrotor unmanned aerial vehicles

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    This thesis presents the theoretical development, simulation study and flight tests of a Lyapunov-based control approach for the Fault Tolerant Control (FTC) of a quadrotor unmanned aerial vehicle (UAV). Based on the derivation of nonlinear model of the dynamics of the quadrotor UAV, a Lyapunov-based control approach with fixed controller gains is proposed and firstly demonstrated through simulations of the quadrotor UAV for handling system parameter uncertainties. Secondly, this proposed Lyapunov-based approach with the selected controller gains is applied as a fault tolerant controller in the framework of a passive Fault Tolerant Control System (FTCS), for handling less severe faults occurring in the quadrotor UAV. Thirdly, the proposed new controller by Lyapunov-based adaptive control method for fault tolerant control of the quadrotor UAV is proposed to handle more severe faults. Finally, the Lyapunov-based control method has been implemented to the test bed, Qball-X4 Unmanned Aerial Vehicle, and the acceptable performances on altitude control have been achieved. In the thesis, simulation and flight testing results demonstrate that the FTCS with the Lyapunov-based approach has certain robustness for most of partial losses. However, the FTCS with Lyapunov-based adaptive control approach has advantages in accommodating more severe faults for, which may not be addressed by the Lyapunov-based approac

    Fault tolerant control of multi-rotor unmanned aerial vehicles using sliding mode based schemes

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    This thesis investigates fault-tolerant control (FTC) for the specific application of small multirotor unmanned aerial vehicles (Unmanned Aerial Vehicle (UAV)s). The fault-tolerant controllers in this thesis are based on the combination of sliding mode control with control allocation where the control signals are distributed based on motors' health level. This alleviates the need to reconfigure the overall structure of the controllers. The thesis considered both the over actuated (sufficient redundancy) and under-actuated UAVs. Three multirotor UAVs have been considered in this thesis which includes a quadrotor (4 rotors), an Octocopter (8 rotors) and a spherical UAV. The non-linear mathematical models for each of the UAVs are presented. One of the main contributions of this thesis is the hardware implementation of the sliding mode Fault Tolerant Control (FTC) scheme on an open-source autopilot microcontroller called Pixhawk for a quadrotor UAV. The controller was developed in Simulink and implemented on the microcontroller using the Matlab/Simulink support packages. A gimbal- based test rig was developed and built to offer a safe test bed for testing control designs. Actual flight tests were done which showed sound responses during fault-free and faulty scenarios. This work represents one of successful implementation work of sliding mode FTC in the literature. Another key contribution of this thesis is the development of the mathematical model of a unique spherical UAV with highly redundant control inputs. The use of novel 8 flaps and 2 rotors configuration of the spherical UAV considered in this thesis provides a unique fault tolerant capability, especially when combined with the sliding mode-based FTC scheme. A key development in the later chapters of the thesis considers fault-tolerant control strategy when no redundancy is available. Unlike many works which consider FTC on quadrotors in the literature (which can only handle faults), the proposed schemes in the later chapters also include cases when failures also occur converting the system to an under actuated system. In one chapter, a bespoke Linear Parameter Varying (LPV) based controller is developed for a reduced attitude dynamics system by exploiting non-standard equation of motions which relates to position acceleration and load factor dynamics. This is unique as compared to the typical Euler angle control (roll, pitch and yaw angle control). In the last chapter, a fault-tolerant control scheme which can handle both the over and under actuated system is presented. The scheme considers an octocopter and can be used in fault-free, faulty and failure conditions up to two remaining motors. The scheme exploits the differential flatness property, another unique property of multirotor UAVs. This allows both inner loop and outer loop controller to be designed using sliding mode (as opposed to many sliding mode FTC in the literature, which only considers sliding mode for the inner loop control)

    Controlling a drone: Comparison between a based model method and a fuzzy inference system

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    International audienceThe work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner, and helicoidal) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. Simulation results and a comparison with a static feedback linearization controller (SFL) are presented and discussed. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a breakdown of an engine as well as a gust of wind

    Enhancing VTOL Multirotor Performance With a Passive Rotor Tilting Mechanism

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    This article discusses the benefits of introducing a simple passive mechanism to enable rotor tilting in Vertical Take-Off and Landing (VTOL) multirotor vehicles. Such a system is evaluated in relevant Urban Air Mobility (UAM) passenger transport scenarios such as hovering in wind conditions and overcoming rotor failures. While conventional parallel axis multirotors are underactuated systems, the proposed mechanism makes the vehicle fully actuated in SE(3), which implies independent cabin position and orientation control. An accurate vehicle simulator with realistic parameters is presented to compare in simulation the proposed architecture with a conventional underactuated VTOL vehicle that shares the same physical properties. In order to make fair comparisons, controllers are obtained solving an optimization problem in which the cost function of both systems is chosen to be equivalent. In particular, the control laws are Linear-Quadratic Regulators (LQR), which are derived by linearizing the systems around hover. It is shown through extensive simulation that the introduction of a passive rotor tilting mechanism based on universal joints improves performance metrics such as vehicle stability, power consumption, passenger comfort and position tracking precision in nominal flight conditions and it does not compromise vehicle safety in rotor failure situations

    ๋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

    Trajectory Tracking and Payload Dropping of an Unmanned Quadrotor Helicopter Based on GS-PID and Backstepping Control

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    Two useful control techniques, the Gain-Scheduled Proportional-Integral-Derivative (GS-PID) control and backstepping control, have been applied by using quadrotor Unmanned Aerial Vehicle (UAV) in the applications of trajectory tracking and payload dropping operations in this thesis. These control algorithms are analyzed and verified through software simulations and experimental tests. The dynamic model of the quadrotor UAV is firstly established using Newton-Euler laws. The quadrotor comes with a symmetric, nonlinear and multiple-input-multiple output (MIMO) dynamic model. The GS-PID control algorithm is implemented firstly in take-off, trajectory tracking, payload dropping, and landing periods of flight in trajectory tracking and payload dropping scenarios. Unlike other control algorithms that tend to linearize nonlinear systems, backstepping works without cancelling the nonlinearities in the system. This leads to more flexible designs of the control model. The backstepping control is implemented in this thesis for better performance of the quadrotor UAV for the two scenarios as well. Both control algorithms are implemented on the parameters of an unmanned quadrotor helicopter platform known as Qball-X4 available at the Networked Autonomous Vehicles Lab (NAVL) of Concordia University. Using MATLAB/Simulink to build the simulation control model, the flight simulation of the Qball-X4 is carried out for the trajectory tracking and the payload dropping. In order to further investigate these two control approaches, the Qball-X4 is used for experimental verification on payload dropping performance. The results indicate that both algorithms can obtain acceptable performance, but the backstepping controller proves to be a better performed one

    Multi-agent Collision Avoidance Using Interval Analysis and Symbolic Modelling with its Application to the Novel Polycopter

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    Coordination is fundamental component of autonomy when a system is defined by multiple mobile agents. For unmanned aerial systems (UAS), challenges originate from their low-level systems, such as their flight dynamics, which are often complex. The thesis begins by examining these low-level dynamics in an analysis of several well known UAS using a novel symbolic component-based framework. It is shown how this approach is used effectively to define key model and performance properties necessary of UAS trajectory control. This is demonstrated initially under the context of linear quadratic regulation (LQR) and model predictive control (MPC) of a quadcopter. The symbolic framework is later extended in the proposal of a novel UAS platform, referred to as the ``Polycopter" for its morphing nature. This dual-tilt axis system has unique authority over is thrust vector, in addition to an ability to actively augment its stability and aerodynamic characteristics. This presents several opportunities in exploitative control design. With an approach to low-level UAS modelling and control proposed, the focus of the thesis shifts to investigate the challenges associated with local trajectory generation for the purpose of multi-agent collision avoidance. This begins with a novel survey of the state-of-the-art geometric approaches with respect to performance, scalability and tolerance to uncertainty. From this survey, the interval avoidance (IA) method is proposed, to incorporate trajectory uncertainty in the geometric derivation of escape trajectories. The method is shown to be more effective in ensuring safe separation in several of the presented conditions, however performance is shown to deteriorate in denser conflicts. Finally, it is shown how by re-framing the IA problem, three dimensional (3D) collision avoidance is achieved. The novel 3D IA method is shown to out perform the original method in three conflict cases by maintaining separation under the effects of uncertainty and in scenarios with multiple obstacles. The performance, scalability and uncertainty tolerance of each presented method is then examined in a set of scenarios resembling typical coordinated UAS operations in an exhaustive Monte-Carlo analysis

    Adaptive and Optimal Motion Control of Multi-UAV Systems

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    This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed

    Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

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    The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

    Advanced Feedback Linearization Control for Tiltrotor UAVs: Gait Plan, Controller Design, and Stability Analysis

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    Three challenges, however, can hinder the application of Feedback Linearization: over-intensive control signals, singular decoupling matrix, and saturation. Activating any of these three issues can challenge the stability proof. To solve these three challenges, first, this research proposed the drone gait plan. The gait plan was initially used to figure out the control problems in quadruped (four-legged) robots; applying this approach, accompanied by Feedback Linearization, the quality of the control signals was enhanced. Then, we proposed the concept of unacceptable attitude curves, which are not allowed for the tiltrotor to travel to. The Two Color Map Theorem was subsequently established to enlarge the supported attitude for the tiltrotor. These theories were employed in the tiltrotor tracking problem with different references. Notable improvements in the control signals were witnessed in the tiltrotor simulator. Finally, we explored the control theory, the stability proof of the novel mobile robot (tilt vehicle) stabilized by Feedback Linearization with saturation. Instead of adopting the tiltrotor model, which is over-complicated, we designed a conceptual mobile robot (tilt-car) to analyze the stability proof. The stability proof (stable in the sense of Lyapunov) was found for a mobile robot (tilt vehicle) controlled by Feedback Linearization with saturation for the first time. The success tracking result with the promising control signals in the tiltrotor simulator demonstrates the advances of our control method. Also, the Lyapunov candidate and the tracking result in the mobile robot (tilt-car) simulator confirm our deductions of the stability proof. These results reveal that these three challenges in Feedback Linearization are solved, to some extents.Comment: Doctoral Thesis at The University of Toky
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