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無人直昇機是指在沒有搭載駕駛員下，能自動飛行的直昇機。這種飛行器除了不用令駕駛員冒上性命風險，亦由於其獨特飛行能力，例如空中旋停、急彎打轉等，令無人直昇機在執行危險任務上有著重要的優勢。但同時，由於沒有了駕駛員的關係，這種飛行器持續面對著一系列的技術難點。當中包括：（一）在飛行上，無人直昇機帶有一種未能確解的動態反應，偶合在主控軸上，令動態變化的預測變得困難；（二）絶大部份無人直昇機的導航倚賴全球定位儀，但這種定位儀卻極易受干擾，影響飛行安全；（三）由於每次任務的需要，機上搭載物的位置及多寡令飛行器上的重力分佈經常改變，令自動控制器需面密集的手動調度及系統識別。 / 對於無人直昇機的動態模型建構，我們提出了一種新辦法令過往未能確解的偶合動態反應，能被準確的量化及分析，因而使這種模型能更準確預測無人直昇機的動態變化。我們的辦法針對過往一種普遍使用但不能預測偶合動態反應，名為「遲相」的模型方法，並提出以主旋槳上的陀螺現像，去描述無人直昇機上的偶合動態反應。另外，我們的模型分析顯示出準確預測無人直昇機的動態反應需要識別一系列難以度測而又經常變化的系統參數，因此我們提出以一個相對隱性模型的辦法對解決往後有關導航及控制的問題更有利。 / 有關無人直昇機的導航，我們提出以慣性感測器去補償全球定位儀因干擾及訊號異常導致的感測數據中斷。通過一系列的噪音處理，我們的辦法能從慣性感測器的加速度讀數轉化為位移，從而令無人直昇機能在突如其來的全球定位儀中斷作出即時反應，避免因位置讀數中斷而墜毀。除此以外，我們亦提供全自動參數調節算法，令整個噪音處理能快速應用於不同慣性感測器的設置上。 / 有關無人直昇機的控制，由於一般需要廣泛的系統建模及識別，因此我們提出以「能力轉移」的概念，透過駕駛員的示範，提取控制直昇機的技巧從而令機器無需建構準確和全面的動態模型下，能準確控制無人直昇機。我們通過建立一個簡明的反饋模型，利用的駕駛員的示範而自動配置出最適合的參數去控制無人直昇機。 / 從更準確的動態模型分析，到抗干擾導航及新型控制器的設計，整個研究為無人直昇機的發展提出了多方面的觀點及解決辦法。與此同時，我們的最終目的是希望為無人直昇機的日常應用上掃除因為缺少了駕駛員而做成的障礙，令無人直昇機能真正以最少的人為干涉而達至全面的無人操作。 / Unmanned helicopters hold both tremendous potential and challenges. Without risking the lives of human pilots, these vehicles exhibit agile movement and the ability to hover and hence open up a wide range of applications in the hazardous situations. Sparing human lives, however, comes at a stiff price for technology. Some of the key difficulties that arise in these challenges are: (i) There are unexplained cross-coupled responses between the control axes on the hingeless helicopters that have puzzled researchers for years. (ii) Most, if not all, navigation on the unmanned helicopters relies on Global Navigation Satellite Systems (GNSSs), which are susceptible to jamming. (iii) It is often necessary to accommodate the re-configurations of the payload or the actuators on the helicopters by repeatedly tuning an autopilot, and that requires intensive human supervision and/or system identification. / For the dynamics modeling and analysis, we present a comprehensive review on the helicopter actuation and dynamics, and contributes toward a more complete understanding on the on-axis and off-axis dynamical responses on the helicopter. We focus on a commonly used modeling technique, namely the phase-lag treatment, and employ a first-principles modeling method to justify that (i) why that phase-lag technique is inaccurate, (ii) how we can analyze the helicopter actuation and dynamics more accurately. Moreover, these dynamics modeling and analysis reveal the hard-to-measure but crucial parameters on a helicopter model that require the constant identifications, and hence convey the reasoning of seeking a model-implicit method to solve the state estimation and control problems on the unmanned helicopters. / For the state estimation, we present a robust localization method for the unmanned helicopter against the GNSS outage. This method infers position from the acceleration measurement from an inertial measurement unit (IMU). In the core of our method are techniques of the sensor error modeling and the filtering method for the sensor noise compensation. Moreover, we provide a fully automatic algorithm to tune our method. Finally, we evaluate our method on an instrumented gasoline helicopter. Experiments show that the technique enables the robust positioning of flying helicopters when no GNSS measurement is available. / The design of an autopilot for an unmanned helicopter is made difficult by its nonlinear, coupled and non-minimum phase dynamics. Here, we consider a reinforcement learning approach to transfer motion skills from human to machine, and hence to achieve autonomous flight control. By making efficient use of a series of state-and-action pairs given by a human pilot, our algorithm bootstraps a parameterized control policy and learns to hover and follow trajectories after one manual flight. One key observation our algorithm is based on is that, although it is often difficult to retrieve the human pilots’ hidden desiderata that formulate their state-feedback mechanisms in controlling the helicopters, it is possible to intercept the states of a helicopter and the actions by a human pilot and then to fit both into a model. We demonstrate the performance of our learning controller in experiments. / The results described in this dissertation shed new and important light on the technology necessary to advance the current state of the unmanned helicopters. From a comprehensive dynamics modeling that addresses perplexing cross-couplings on the unmanned helicopters, to a robust state estimation against GNSS outage and a learn-from-scarcesample control for an unmanned helicopter, we provide a starting point for the cultivation of the next-generation unmanned helicopters that can operate with the least possible human intervention. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Lau, Tak Kit. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 182-198). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The State-of-the-Art --- p.5 / Chapter 1.2 --- Problemand Approach --- p.8 / Chapter 1.3 --- Contributions and Organization of Chapters --- p.13 / Chapter 2 --- Actuation and Dynamics of Helicopter --- p.16 / Chapter 2.1 --- Introduction --- p.17 / Chapter 2.2 --- Input-Output Relation of Hingeless Helicopters --- p.20 / Chapter 2.3 --- Helicopter Dynamics --- p.30 / Chapter 2.3.1 --- Aerodynamics --- p.34 / Chapter 2.3.2 --- Discrepancy and Conventional Treatment --- p.36 / Chapter 2.3.3 --- Gyroscopically InducedMoment --- p.38 / Chapter 2.3.4 --- Example of Pitching Forward --- p.42 / Chapter 2.4 --- Empirical Evaluations --- p.44 / Chapter 2.4.1 --- Numerical Simulation --- p.45 / Chapter 2.4.2 --- Experiments --- p.47 / Chapter 2.5 --- Summary --- p.48 / Chapter 3 --- State Estimation against GNSS Outage --- p.53 / Chapter 3.1 --- Introduction --- p.55 / Chapter 3.2 --- Sensor Noises --- p.62 / Chapter 3.2.1 --- Sensitivity Analysis of Estimation Accuracy --- p.63 / Chapter 3.2.2 --- Sensor Error Characteristics --- p.68 / Chapter 3.2.3 --- Random Walk Acceleration Noise --- p.71 / Chapter 3.3 --- State Estimation Algorithm --- p.76 / Chapter 3.3.1 --- Process and Measurement Models --- p.79 / Chapter 3.3.2 --- Selection of OperationModes --- p.83 / Chapter 3.3.3 --- Prioritized Propagation of States --- p.85 / Chapter 3.4 --- Automatic Tuning of Filter Parameters --- p.87 / Chapter 3.4.1 --- Performance Index --- p.88 / Chapter 3.4.2 --- Tuning Algorithm --- p.90 / Chapter 3.5 --- Experimental Results --- p.93 / Chapter 3.6 --- Summary --- p.97 / Chapter 4 --- Model-based Control for Unmanned Helicopters --- p.106 / Chapter 4.1 --- Introduction --- p.107 / Chapter 4.2 --- Effect of Center of Gravity --- p.108 / Chapter 4.3 --- Design of Attitude Controller --- p.110 / Chapter 4.4 --- Augmentation in Controller and Experimental Results --- p.113 / Chapter 4.5 --- Summary --- p.117 / Chapter 5 --- Learning Control for Unmanned Helicopters --- p.120 / Chapter 5.1 --- Introduction --- p.121 / Chapter 5.2 --- RelatedWork --- p.124 / Chapter 5.3 --- Preliminary --- p.129 / Chapter 5.3.1 --- Derivation of Policy Gradient --- p.133 / Chapter 5.4 --- Controller Design --- p.133 / Chapter 5.4.1 --- Learning Dynamics --- p.135 / Chapter 5.4.2 --- Learning Control --- p.137 / Chapter 5.5 --- Performance Analysis --- p.141 / Chapter 5.5.1 --- Baseline --- p.141 / Chapter 5.5.2 --- Numerical Analysis --- p.145 / Chapter 5.6 --- Convergence Analysis --- p.152 / Chapter 5.7 --- Evaluation --- p.153 / Chapter 5.7.1 --- Simulation - Acrobot --- p.153 / Chapter 5.7.2 --- Simulation - Fixed-wing Aircraft --- p.154 / Chapter 5.7.3 --- Experiment - RC Car --- p.158 / Chapter 5.7.4 --- Experiment - Helicopter --- p.161 / Chapter 5.8 --- Summary --- p.169 / Chapter 6 --- Conclusion --- p.171 / Chapter A --- List of Publications & Awards --- p.177 / Chapter A.0.1 --- Publications --- p.177 / Chapter A.0.2 --- Awards --- p.181 / Bibliography --- p.182

相對於在地面上行走的機器人, 自動無人飛機擁有高自由度及靈活操控性。在對人類有危險的環境中，無人飛機更特別可以全自動或供操作員安全地遙距控制來執行危險工作。但無線連結不能確保安全及穩定，所以基於安全性考慮自動無人機上必需擁有自主運算能力以用於自動定位，返航及避障。 / 可是制作全自動無人機有很多的限制，如小型無人飛機上只能使用較小型馬達，載重十分少，故能被作為主要的計算機也不能使用強大的圖像處理器(GPU)，所以只能使用運算能力較低的一般處理器(CPU)。此外，傳統的圖像處理定位方法也只能在強大硬件下實時運行，這種種的限制加起來使得小型無人飛機的自動定位成為難題。 / 一般現在的無人機，例如作為航空拍攝的，都是使用簡單而小型的全球定位系統感測器，所需要的運算器硬件要求也不高。但是這些無人飛機卻不能被用於室內或靠近建築物的地方，因為衛星定位信號會被多重反射甚至是阻隔開來。此外，全球定位系統普不能給予無人機有關機體附近環境的訊息，例如視覺特徵及三維結構等。所以無人機並沒有能力避開環境中的障礙物或甚至危害人身安全。 / 我們設計了飛行控制器，內含恣態感測組件，光流傳感器及高度計等元件作控制及感測。我們也提出了一個全自動的校準方法用於低成本的恣態感測器，當中並不需要任何人為的操作或昂貴和精準的的校準儀器。 / 除了恣態感測組件用於機體動作測量外，環境資訊都是對自動飛行有很大用的。我們所建構的無人飛機平台上使用了多種不同的運算組件及視覺定位算法，目的在於提拱一個有效,快速且能於飛機上裝置的電腦實時運算的定位系統。 / 從本論文所展示的結果中可得悉我們在無人機的及展，包括如何把低成本的恣態感測器校正的方法以增加準確性及從環境視覺特徵及三維結構幫助計算無人機自身位置的定位方法。我們希望本研究論文能對下一世代的自動無人機發展有幫助。 / Unmanned Aerial Vehicles (UAVs) process great maneuverability over ground vehicles, legged and tracked robots. UAVs can do more dangerous tasks where human can safely tele-operate from far away. However stable communication channels may not be guaranteed and therefore autonomous onboard processing for safety-concerned functions such as localization, homing or obstacles avoidance are essential. / UAVs have more limitations than others, typically the payload weight is significantly reduced compared to ground vehicles with powerful motors. Other limitations such as limited computing power for light-weight computer modules being put on-board lend the difficulty to the localization and control of the agile UAVs using traditional computationally exhausting Visual SLAM approaches. / Although the traditional simple GPS-Inertial approaches are beautiful that the hardware requirement is not so demanding, they cannot be applied in indoor or near indoor environments due to signal jamming and multiple-reflection. Additionally, GPS cannot provide any information about the environment arround the UAV so it raises risk for the UAV to danger such as obstacles collision which may cause serious injury to human. / To implement an autonomous aerial vehicle we built our flight-controller with a low-cost inertial measurement unit (IMU) and optical ow sensor and altimeter for motor control and sensing. The low-cost IMU suffers from bad measurement bias and drifts. We proposed a fully autonomous calibration method where no human intervention nor precise calibration equipment is needed. / Besides the IMU which can only provide body motion sensing, the perception about the environment is required for autonomous flying and avoid obstacles. We implemented a visual localization algorithm which runs efficiently in realtime on our light-weight and low-cost computer module. We have built a set of UAV system with an RGB-D Kinect sensor, which fuse different sensor measurements together for the localization. / The results presented in this thesis show a new possiblity to advance current development on Unmanned Aerial Vehicles. From the autonomous calibration method for low-cost IMU to improve accuracy orientation estimation to 3D localization using the perception of the environment features and structures from RGB-D sensor. At the same time we hope that our experience on the hardware building and real-time localization algorithm implementations can help boosting the advancement of the next generation of autonomous aerial vehicles. / Cheuk, Chi Ming. / Thesis M.Phil. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 88-96). / Abstracts also in Chinese. / Title from PDF title page (viewed on 05, October, 2016). / Cheuk, Chi Ming. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

Lin, Kai Wun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 120-127). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview of the UAV Project --- p.1 / Chapter 1.2 --- Challenges on Vision System for UAV --- p.2 / Chapter 1.3 --- Contributions of this Work --- p.4 / Chapter 1.4 --- Organization of Thesis --- p.6 / Chapter 2 --- Image Sensor Selection and Evaluation --- p.8 / Chapter 2.1 --- Image Sensor Overview --- p.8 / Chapter 2.1.1 --- Comparing Sensor Features and Performance --- p.9 / Chapter 2.1.2 --- Rolling Shutter vsGlobal Shutter --- p.10 / Chapter 2.2 --- Sensor Evaluation through USB Peripheral --- p.11 / Chapter 2.2.1 --- Interfacing Image Sensor and USB Controller --- p.12 / Chapter 2.2.2 --- Image Sensor Configuration --- p.14 / Chapter 2.3 --- Image Data Transmitting and Processing --- p.17 / Chapter 2.3.1 --- Data Transfer Mode and Buffering on USB Controller --- p.18 / Chapter 2.3.2 --- Demosaicking of Bayer Image Data --- p.20 / Chapter 2.4 --- Splitting Images and Exposure Problem --- p.22 / Chapter 2.4.1 --- Buffer Overflow on USB Controller --- p.22 / Chapter 2.4.2 --- Image Luminance and Exposure Adjustment --- p.24 / Chapter 3 --- Embedded System for Vision Processing --- p.26 / Chapter 3.1 --- Overview of the Embedded System --- p.26 / Chapter 3.1.1 --- TI OMAP3530 Processor --- p.27 / Chapter 3.1.2 --- Gumstix Overo Fire Computer-on-Module --- p.27 / Chapter 3.2 --- Interfacing Camera Module to the Embedded System --- p.28 / Chapter 3.2.1 --- Image Signal Processing Subsystem --- p.29 / Chapter 3.2.2 --- Camera Module Adapting Board --- p.30 / Chapter 3.2.3 --- Image Sensor Driver and Program Development --- p.31 / Chapter 3.3 --- View-stabilizing Biaxial Camera Platform --- p.34 / Chapter 3.3.1 --- The New Camera System iv --- p.35 / Chapter 3.3.2 --- View-stabilizing Pan-tilt Platform --- p.41 / Chapter 3.4 --- Overall System Architecture and UAV Integration --- p.46 / Chapter 4 --- Target Tracking and Geo-locating --- p.50 / Chapter 4.1 --- Camera Calibration --- p.51 / Chapter 4.1.1 --- The Perspective Camera Model --- p.51 / Chapter 4.1.2 --- Camera Lens Distortions --- p.53 / Chapter 4.1.3 --- Calibration Toolbox and Results --- p.54 / Chapter 4.2 --- Selection of Object Features and Trackers --- p.56 / Chapter 4.2.1 --- Harris Corner Detection --- p.58 / Chapter 4.2.2 --- Color Histogram --- p.59 / Chapter 4.2.3 --- KLT and Mean-shift Tracker --- p.59 / Chapter 4.3 --- Target Auto-centering --- p.64 / Chapter 4.3.1 --- Formulation of the PID Controller --- p.65 / Chapter 4.3.2 --- Control Gain Settings and Tuning --- p.69 / Chapter 4.4 --- Geo-locating of Tracked Target --- p.69 / Chapter 4.4.1 --- Coordinate Frame Transformation --- p.70 / Chapter 4.4.2 --- Depth Estimation and Target Locating --- p.74 / Chapter 4.5 --- Results and Discussion --- p.77 / Chapter 5 --- Real-time Aerial Mosaic Building --- p.89 / Chapter 5.1 --- Motion Model Selection --- p.90 / Chapter 5.1.1 --- Planar Perspective Motion Model --- p.90 / Chapter 5.2 --- Feature-based Image Alignment --- p.91 / Chapter 5.2.1 --- Image Preprocessing --- p.91 / Chapter 5.2.2 --- Feature Extraction and Matching --- p.92 / Chapter 5.2.3 --- Image Alignment using RANSAC Algorithm --- p.94 / Chapter 5.3 --- Image Composition --- p.95 / Chapter 5.3.1 --- Image Blending with Distance Map --- p.96 / Chapter 5.3.2 --- Overall Stitching Process --- p.98 / Chapter 5.4 --- Mosaic Simulation using Google Earth --- p.99 / Chapter 5.5 --- Results and Discussion --- p.100 / Chapter 6 --- Conclusion and Further Work --- p.108 / Chapter A --- System Schematics --- p.111 / Chapter B --- Image Sensor Sensitivity --- p.118 / Bibliography --- p.120

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Prasad, JVR; Committee Member: Costello, Mark; Committee Member: Johnson, Eric; Committee Member: Schrage, Daniel; Committee Member: Vela, Patricio. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (MTech (Mechanical Engineering))--Cape Peninsula University of Technology, 2010. / The Cape Peninsula University of Technology (CPUT) Advanced Manufacturing and Technology Laboratory (AMTL) developed an Unmanned Aerial Vehicle (UAV) Technology Demonstrator for the purpose of testing and maturing adaptronic devices. Extending the flight envelope of this unmanned aerial vehicle by increasing its range and endurance is the next step in its development. A seamless variable angle of incidence (sVAI) morphing wing is proposed to increase the lift with little coupling to drag during takeoff; and decrease the drag with little effect on lift during climb, thus increasing the total flight performance of the aircraft. CAD models of the conceptualized sVAI wing and a conventional (CON) wing, as used on the Technology Demonstrator, were modeled. Numerical analyses on these CAD models showed that the sVAI wing concept at a 4° twist decreased the ground roll distance and stall velocity by ±17% and ±31% respectively, as compared to the CON wing in standard takeoff configuration. This allowed for ± 11.7% less power required for takeoff allowing the aircraft to get to its operational altitude quicker, thus saving fuel and reducing energy losses; and increasing range and endurance. The results also showed that the sVAI wing concept could reduce the drag during climb by ± 14%, but the lift is also proportionately reduced thus having little improvement on the climb phase of flight performance. A prototype of the morphing wing was then conceptualized and designed, using a 3D CADmodeler, and then manufactured. The product development chain produced for this morphing wing included two rapid prototyping machines and reverse engineering technologies. The chain allowed for the rapid manufacturing of light weight and intricate parts. The manufactured wing is then incorporated into a test rig to compare the actual morphing ability of the prototype to the theoretical morphing ability of the CADmodel, and thus make flight performance predictions of the actual vehicle. 3D scans were taken of the prototype and then converted to 3D CADfiles. The geometrical and topographical deformation of the prototype was then compared to that of the CAD model showing an average difference of ±1.2% and ±3% at maximum positive and negative configurations, respectively. This allowed one to make the prediction that the sVAI wing will increase the performance of the Technology Demonstrator.

The fact that pilots are not physically situated in the aircraft for UAS operations makes the current standards applicable to manned aircraft not suitable for UAS operations (FAA, 2013). FAA (2013:18) states that ―removing the pilot from the aircraft creates a series of performance considerations between manned and unmanned aircraft that need to be fully researched and understood to determine acceptability and potential impact on safe operations in the NAS. According to ERSG (2013), not all technologies necessary to ensure the safe integration of civil UASs into civilian airspace are available today. The extrapolation that can be made based on the above arguments is that advancement of UAS technologies will more likely have a significant bearing on the safe integration of UASs into civilian airspace. Therefore, as an identified research gap, the research/main objective of this research is to identify future technological factors affecting Unmanned Aircraft Systems in the Republic of South Africa leading towards the year 2025.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: In this project, system identification is done on the Modular Unmanned Aerial Vehicle (UAV). This is necessary to perform fault detection and isolation, which is part of the Fault Tolerant Control research project at Stellenbosch University. The equations necessary to do system identification are developed. Various methods for system identification is discussed and the regression methods are implemented. It is shown how to accommodate a sudden change in aircraft parameters due to a fault. Smoothed numerical differentiation is performed in order to acquire data necessary to implement the regression methods. Practical issues regarding system identification are discussed and methods for addressing these issues are introduced. These issues include data collinearity and identification in a closed loop. The regression methods are implemented on a simple roll model of the Modular UAV in order to highlight the various difficulties with system identification. Different methods for accommodating a fault are illustrated. System identification is also done on a full nonlinear model of the Modular UAV. All the parameters converges quickly to accurate values, with the exception of Cl R , CnP and Cn A . The reason for this is discussed. The importance of these parameters in order to do Fault Tolerant Control is also discussed. An S-function that implements the recursive least squares algorithm for parameter estimation is developed. This block accommodates for the methods of applying the forgetting factor and covariance resetting. This block can be used as a stepping stone for future work in system identification and fault detection and isolation. / AFRIKAANSE OPSOMMING: In hierdie projek word stelsel identifikasie gedoen op die Modulêre Onbemande Vliegtuig. Dit is nodig om foutopsporing en isolasie te doen wat ’n deel uitmaak van fout verdraagsame beheer. Die vergelykings wat nodig is om stelsel identifikasie te doen is ontwikkel. Verskeie metodes om stelsel identifikasie te doen word bespreek en die regressie metodes is uitgevoer. Daar word gewys hoe om voorsiening te maak vir ’n skielike verandering in die vliegtuig parameters as gevolg van ’n fout. Reëlmatige numeriese differensiasie is gedoen om data te verkry wat nodig is vir die uitvoering van die regressie metodes. Praktiese kwessies aangaande stelsel identifikasie word bespreek en metodes om hierdie kwessies aan te spreek word gegee. Hierdie kwessies sluit interafhanklikheid van data en identifikasie in ’n geslote lus in. Die regressie metodes word toegepas op ’n eenvoudige rol model van die Modulêre Onbemande Vliegtuig om die verskeie kwessies aangaande stelsel identifikasie uit te wys. Verskeie metodes vir die hantering vir ’n fout word ook illustreer. Stelsel identifikasie word ook op die volle nie-lineêre model van die Modulêre Onbemande Vliegtuig gedoen. Al die parameters konvergeer vinnig na akkurate waardes, met die uitsondering van Cl R , CnP and Cn A . Die belangrikheid van hierdie parameters vir fout verdraagsame beheer word ook bespreek. ’n S-funksie blok vir die rekursiewe kleinste-kwadraat algoritme is ontwikkel. Hierdie blok voorsien vir die metodes om die vergeetfaktor en kovariansie herstelling te implementeer. Hierdie blok kan gebruik word vir toekomstige werk in stelsel identifikasie en foutopsporing en isolasie.

Simple, cheap and reliable echo-based ultrasonic ranging systems such as the Polaroid ranging unit are easily applied to indoor applications. However, to measure the range between an unmanned helicopter and a moving ship deck at sea using ultrasound requires a more robust ranging system, because rushing air and breaking water are known ultrasound noise sources. The work of designing, constructing and testing such a system is described in this dissertation. The compact, UAV ready ultrasound transmitter module provides high power, broadband arbitrary signal generation. The separate field-ready receiver is based on a modern embedded Digital Signal Processor (DSP), providing high speed matched-filter correlation processing. Large time-bandwidth signalling is employed to maximise the signal to noise ratio of the ranging system. Synthesised experiments demonstrate the ability of the correlation processing to reliably recover timing from signals buried in noise. Real world experiments demonstrate decimetre accuracy with two centimetre resolution, ten metre range and 32Hz refresh rate. A maximum boresight range of up to 38m is supported.

This thesis presents the development of small, inexpensive unmanned aerial vehicles (UAVs) to achieve autonomous fight. Fixed wing hobby model planes are modified and instrumented to form experimental platforms. Different sensors employed to collect the flight data are discussed along with their calibrations. The time constant and delay for the servo-actuators for the platform are estimated. Two different data collection and processing units based on micro-controller and PC104 architectures are developed and discussed. These units are also used to program the identification and control algorithms. Flight control of fixed wing UAVs is a challenging task due to the coupled, time-varying, nonlinear dynamic behaviour. One of the possible alternatives for the flight control system is to use the intelligent adaptive control techniques that provide online learning capability to cope with varying dynamics and disturbances. Neural network based indirect adaptive control strategy is applied for the current work. The two main components of the adaptive control technique are the identification block and the control block. Identification provides a mathematical model for the controller to adapt to varying dynamics. Neural network based identification provides a black-box identification technique wherein a suitable network provides prediction capability based upon the past inputs and outputs. Auto-regressive neural networks are employed for this to ensure good retention capabilities for the model that uses the past outputs and inputs along with the present inputs. Online and offline identification of UAV platforms are discussed based upon the flight data. Suitable modifications to the Levenberg-Marquardt training algorithm for online training are proposed. The effect of varying the different network parameters on the performance of the network are numerically tested out. A new performance index is proposed that is shown to improve the accuracy of prediction and also reduces the training time for these networks. The identification algorithms are validated both numerically and flight tested. A hardware-in-loop simulation system has been developed to test the identification and control algorithms before flight testing to identify the problems in real time implementation on the UAVs. This is developed to keep the validation process simple and a graphical user interface is provided to visualise the UAV flight during simulations. A dual neural network controller is proposed as the adaptive controller based upon the identification models. This has two neural networks collated together. One of the neural networks is trained online to adapt to changes in the dynamics. Two feedback loops are provided as part of the overall structure that is seen to improve the accuracy. Proofs for stability analysis in the form of convergence of the identifier and controller networks based on Lyapunov's technique are presented. In this analysis suitable bounds on the rate of learning for the networks are imposed. Numerical results are presented to validate the adaptive controller for single-input single-output as well as multi-input multi-output subsystems of the UAV. Real time validation results and various flight test results confirm the feasibility of the proposed adaptive technique as a reliable tool to achieve autonomous flight. The comparison of the proposed technique with a baseline gain scheduled controller both in numerical simulations as well as test flights bring out the salient adaptive feature of the proposed technique to the time-varying, nonlinear dynamics of the UAV platforms under different flying conditions.