臺灣博碩士論文加值系統

本研究的目的是開發與驗證適用於無人飛行載具(簡稱UAV)的姿態航向參考系統(簡稱AHRS)。有非常多的感測器都可提供姿態估測所需的資訊，像是加速規、陀螺儀、磁力計、全球衛星定位系統(簡稱GPS)…等等，本研究將使用不同的感測器與演算法找出適用於UAV的姿態估測方法。為了提升姿態估測的性能與可靠度，本研究使用兩種濾波器來進行多感測器整合，分別是二階互補濾波器與基於四元素的增廣型卡爾曼濾波器(簡稱EKF)。為了開發與驗證低價位的AHRS，本研究提出一個可用於低價位AHRS的程序，所使用的工具是一個自行開發的三軸轉動平台。所開發的AHRS由一個三軸的加速規、三個單軸的陀螺儀以及一個三軸的電子羅盤所構成，其中加速規與陀羅儀都是使用低價位微電子機械系統(簡稱MEMS)元件。所採用的感測器校正方法是尺度校正以及最小平方法，校正的方式是透過轉動平台來完成，此轉動平台不但能夠用於感測器校正，還能用於AHRS的性能驗證。最後透過靜態與動態測試驗證感測器與AHRS的性能，這兩個測試也是使用轉動平台來完成。由測試的結果得知所開發的AHRS能夠提供可接受且穩定的姿態，而且證明了這個開發程序的實用性。然而，此低價位AHRS用在使用活塞引擎的小型UAV上則存在著一些問題，其中最主要的問題是震動的影響，此震動的來源為引擎，而且其對低價位感測元件的影響很難消除。為了研究引擎震動對使用不同感測器的姿態估測方法所造成的影響，本研究提出一個適用於小飛機的震動模型，此模型是使用短時傅立葉轉換(簡稱STFT)所推導而得。為了開發一套可用於小型UAV的AHRS，本研究提出一個結合陀螺儀與單天線GPS的姿態整合方法，此方法可以降低引擎震動對陀螺儀所造成的影響，進而提升AHRS的精確度與可靠度。所使用的演算法為基於四元素的EKF，此方法利用陀螺儀所量得的角速率來更新四元素，再使用GPS資訊所計算虛擬姿態來更新濾波器的量測值。經由觀察模擬與實驗的結果得知，即使在陀螺儀因為引擎震動的影響而造成更大漂移以及虛擬姿態因為GPS本身所存在的劇烈雜訊造成姿態誤差的情況下，所提出來的方法仍然同時具有短時間與長時間的精確度。最後，為了驗證所提出的姿態估測方法能夠用於UAV的導航系統上，我們使用飛行模擬軟體設計一套具有自主飛行能力的自動駕駛系統，此自動駕駛由四個模糊控制器所組合而成，分別是高度、俯仰角、航向與側滾角控制器，而且此自動駕駛系統也具備導航點追蹤與軌跡追隨的功能。模擬的結果證明此姿態估測方法結合所設計的自動駕駛系統可以成功地完成UAV自主飛行，即使在風的擾動影響之下仍然具有良好的抗干擾能力。

This study focuses on the development of an attitude and heading reference system (AHRS) for the application to unmanned aerial vehicles (UAV). There are many sensors that can provide the information for attitude estimation such as the accelerometer, gyroscope, magnetometer, global positioning system (GPS) et al. In order to improve the performance and reliability of the developed AHRS, two multi-sensor fusion algorithms are employed including the second-order complementary filter and the quaternion-based extended Kalman filter (EKF) to fuse different sensors. For the development and verification of a low-cost AHRS, a development procedure with the help of a self-developed three-axis rotating platform is proposed. This low-cost AHRS consists of one 3-axis accelerometer, three single-axis gyroscopes, and one 3-axis digital compass. Both the accelerometer and gyroscope triads are based on the micro electro-mechanical system (MEMS) technology, and the digital compass is based on the anisotropic-magnetoresistive (AMR) technology. The calibrations for each sensor triad are accomplished by using the scalar calibration and the least squares methods. With the calibration parameters and the data fusion algorithm for the attitude estimation, the self-developed AHRS demonstrates the capabilities of compensating for the sensor errors and outputting the estimated attitude in real-time. The validation results show that the estimated attitudes of the developed AHRS are within the acceptable region. This verifies the practicability of the proposed development procedure. In addition, there are some practical issues while implementing this low-cost AHRS to small UAVs powered by piston engine which is the major source of vibration. Vibration of the piston engine significantly degrades the accuracy of the inertial measurement unit (IMU), especially for the low-cost sensors that are based on MEMS. Therefore, a vibration model for a small UAV is proposed to examine the influence of vibration on attitude estimation with different sensors. The model is derived based on spectrum analysis with short-time Fourier transform (STFT). The vibration is compared with the drift of the gyroscope in order to examine the impact on the attitude estimation. An attitude estimation method that fuses the gyroscopes and single antenna GPS is proposed to mitigate the influence of engine vibration and gyroscope drift. The quaternion-based EKF is implemented to fuse the sensors. This filter fuses the angular rates measured by the gyroscopes and the pseudo-attitude derived from the GPS velocity to estimate the attitude of the UAV. Simulation and experiment results validate that the proposed method performs well both in short-term and long-term accuracy even though the gyroscopes are affected by drift and vibration noise, while the pseudo-attitude contains severe noise. In order to verify this attitude estimation method on the navigation system of a small UAV, we implement it to the autonomous flight of a small UAV in flight simulator with the help of a proposed autopilot which is based on the fuzzy control. The autopilot consists of four fuzzy logic controllers, which control the altitude, pitch, heading, and roll respectively, and it is capable of performing waypoint navigation and trajectory following. The simulation results demonstrate the successful autonomous flight of this UAV by applying the proposed autopilot and the navigation system, which is based on the proposed sensor fusion algorithm, even under the influence of wind disturbance.

CHINESE ABSTRACT i
ABSTRACT iii
EXTENDED CHINESE ABSTRACT v
ACKNOWLEDGEMENT xvi
LIST OF TABLES xxi
LIST OF FIGURES xxiii
NOMENCLATURE xxvii
Chapter 1 Introduction 1
1.1 AHRS Calibration and Verification 3
1.2 Attitude Determination of the UAV 5
1.3 Flight Control and Navigation of the UAV 8
1.4 AHRS Development in RMRL 9
1.5 Motivation and Objectives 13
1.6 Dissertation Overview 15
Chapter 2 Attitude Estimation Methods with Different Sensors 18
2.1 Gyroscope-Based Method 18
2.2 Accelerometer-Based Method 20
2.3 GPS Velocity-Based Method 21
2.4 Interim Conclusion 24
Chapter 3 Attitude Estimation Based on Multi-Sensor Fusion Algorithms 26
3.1 Second-Order Complementary Filter 26
3.1.1 Fusion of the Accelerometer, Gyroscope, and Magnetometer 26
3.1.2 Fusion of the Gyroscopes and Single Antenna GPS for Small UAV 29
3.2 Quaternion-Based EKF 31
3.2.1 Quaternion Attitude Representation 31
3.2.2 Fusion of the Gyroscopes and single Antenna GPS with Quaternion-Based EKF 33
3.3 Interim Conclusions 34
Chapter 4 Development and Verification of a Low-Cost AHRS Using a Three-Axis Rotating Platform 36
4.1 Low-Cost AHRS Design 37
4.2 Three-Axis Rotating Platform Development 40
4.2.1 Platform Design 40
4.2.2 Platform Calibration 45
4.2.3 Response of the Platform 49
4.3 Application of the Rotating Platform to Sensor Calibrations 52
4.3.1 Sensor Error Model 52
4.3.2 Calibration Procedures and Results 54
4.4 Application of the Rotating Platform to AHRS Calibration and Validation 57
4.4.1 AHRS Calibration 57
4.4.2 AHRS Validation 58
4.5 Experimental Results and Discussion 61
4.5.1 Results of Sensor Calibration 61
4.5.2 Results of AHRS validation 61
4.6 Interim Conclusions 62
Chapter 5 Vibration Modeling for Small UAVs 65
5.1 Modeling Vibration for Small UAVs 65
5.2 Influence of Vibration on Attitude Estimation Methods 71
5.3 Interim Conclusions 75
Chapter 6 Implementation of the Gyroscopes and Single Antenna GPS to Small UAV 76
6.1 AHRS Verification in Flight Simulations 77
6.1.1 Simulation Setup 78
6.1.2 Simulation Results 79
6.2 AHRS Verification in Flight Experiment 85
6.2.1 Experiment Setup 85
6.2.2 Experiment Results 88
6.3 Interim Conclusions 91
Chapter 7 Application of Fuzzy Logic Controller and Pseudo-Attitude to Autonomous Flight of Small UAV 92
7.1 Autopilot Design 92
7.1.1 Flight Control Scheme 93
7.1.2 Fuzzy Logic Controller 95
7.2 Flight Simulations 98
7.2.1 Simulation Scheme 99
7.2.2 Simulation Setup 100
7.3 Results and Discussion 101
7.4 Interim Conclusions 106
Chapter 8 Conclusions and Future Works 108
8.1 Summary of Thesis Contributions 108
8.1.1 Low-Cost AHRS Development 108
8.1.2 Engine Vibration Modeling and Noise Identification 109
8.1.3 Implementation of the AHRS for small UAVs by Fusing the Gyroscopes and Single Antenna GPS 109
8.2 Future Works and Potential Application 110
8.3 Conclusions 111
REFERENCES 113
APPENDIX A 120
VITA 125
PUBLICATION LIST 126

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