M.Ing. / In this study the design of a multivariable inner loop controller for a helicopter, which is based upon pilot-in-the-Ioop considerations, is proposed. A study of previous research regarding pilot-in-the-Ioop behaviour was made. Consequently another study was conducted on previous research regarding the interaction between the pilot, helicopter and the environment. With typical pilot behaviour known, the .emphasis of the study shifted to the helicopter. The effect of speed and controller configuration changes on the dynamic behaviour of the helicopter was analysed. Design assumptions were made which are based upon the crossover model of the pilot. Standard frequency domain and time domain techniques for example a-plane analysis, root loci, pole zero contours, Bode diagrams, state space formulations and step responses were used for analysis and synthesis. Helicopter models and controller models were defined and linked in the state space to form a combined state space model. Inner loop control is the control of rotational movements of the airframe. Inner loop control is sub-divided into damping and stability augmentation control which is achieved by means of rotational rate feedback, attitude hold control which is achieved by means of attitude and rate feedback, control augmentation which is achieved by means of feedforward and additional rate feedback, de-coupling which is achieved by means of cross-feedforward and turn coordination which is achieved by means of cross-feedback. The effect of loop closure in one axis on the dynamic behaviourof the other axes was analysed by means of sequential loop closure techniques. Both the damping and stability augmentation controller and the attitude hold controller were flight tested. Conclusions made from the flight test results led to updates to the initial design assumptions. From these flight tests definite pilot preferances became clear regarding the controller configuration to be used for different flight regimes. The techniques used in this study was compared with some of the most rescent techniques used internationally. Problem areas were identified and proposals made regarding possible future research. Although many new theories exist, it was found that the s-plane is still used by many helicopter control experts. For this reason this study can be regarded as a good foundation for future research on controllers for helicopters.

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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

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Lau, Tak Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 168-176). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.18 / Chapter 1.1 --- Motivation and Problem Statement --- p.18 / Chapter 1.2 --- Literature Review --- p.19 / Chapter 1.2.1 --- Avionics Design --- p.19 / Chapter 1.2.2 --- Controller Design --- p.21 / Chapter 1.2.3 --- Dynamics Analysis --- p.23 / Chapter 1.2.4 --- State Estimation for GNSS Outage --- p.24 / Chapter 1.3 --- Outline --- p.25 / Chapter 2 --- Actuation Dynamics --- p.26 / Chapter 2.1 --- Mcchanism Of The Rotor --- p.27 / Chapter 2.2 --- Mcchanism Of Swashplate And Rotor --- p.28 / Chapter 2.3 --- Numerical Analysis Of Cyclic Pitch Angle --- p.31 / Chapter 2.4 --- Helicopter Dynamics --- p.33 / Chapter 2.4.1 --- Aerodynamic Forces And Moments --- p.35 / Chapter 2.4.2 --- Aerodynamic Drag --- p.36 / Chapter 2.4.3 --- Incremental Lift --- p.36 / Chapter 2.4.4 --- Tail Rotor Thrust And Moment --- p.36 / Chapter 2.4.5 --- Deadweight And Moment --- p.37 / Chapter 2.5 --- The Conventional Inadequacy Of Adding A 90° Phase-Lag --- p.38 / Chapter 2.6 --- The Gyroscopic Effect In Helicopter Dynamics --- p.39 / Chapter 2.6.1 --- How Precession Works --- p.43 / Chapter 2.6.2 --- The Analytical Form --- p.45 / Chapter 2.6.3 --- Numerical Analysis Of The Gyroscopic Effect --- p.48 / Chapter 3 --- State Estimation For GNSS Outage --- p.52 / Chapter 3.1 --- GNSS Error And UAV Failure --- p.52 / Chapter 3.2 --- "Kalman Filter, And The Extended Kalman Filter" --- p.53 / Chapter 3.3 --- Unscented Kalman Filter --- p.54 / Chapter 3.4 --- Process And Measurement Model --- p.55 / Chapter 3.4.1 --- The IMU Driven Model And Sensor Error --- p.57 / Chapter 3.5 --- Modifications To The Model And UKF Algorithm --- p.62 / Chapter 3.5.1 --- Acceleration White Noise Bias (AWNB) --- p.62 / Chapter 3.5.2 --- Acceleration Scale (AS) --- p.64 / Chapter 3.5.3 --- Prioritized Propagation Of States (PPS) --- p.64 / Chapter 3.5.4 --- Performance Of The Proposed Enhancements --- p.66 / Chapter 3.5.5 --- Tripled Percentage Reduction Of Position RMSE When Using PPS With AWNB --- p.73 / Chapter 4 --- Autopilot For Attitude Stabilization --- p.84 / Chapter 4.1 --- Oil Test Bondi --- p.85 / Chapter 4.2 --- On Unconstrained Flight --- p.88 / Chapter 4.2.1 --- Tracking Reference Problem --- p.88 / Chapter 4.2.2 --- An Alternative To PID Attitude Control --- p.91 / Chapter 4.2.3 --- The Proposed Hierarchical PD Controller --- p.91 / Chapter 4.2.4 --- Stability Analysis --- p.92 / Chapter 4.2.5 --- Hierarchy Of The Varying Tracking Reference --- p.95 / Chapter 4.2.6 --- Asymptotical Stability And Robustness --- p.99 / Chapter 4.2.7 --- Experiment and Performance Of The Proposed Controller --- p.101 / Chapter 5 --- Avionics And Test Bench Design --- p.105 / Chapter 5.1 --- Avionics Design --- p.105 / Chapter 5.1.1 --- Design Essentials --- p.107 / Chapter 5.1.2 --- Synchronization Of Commands --- p.107 / Chapter 5.1.3 --- Normalization Of Servomechanism Commands --- p.110 / Chapter 5.2 --- Test Bench Design --- p.110 / Chapter 5.2.1 --- The Idea --- p.111 / Chapter 5.2.2 --- Concern --- p.111 / Chapter 5.2.3 --- Test Bench Design Options --- p.112 / Chapter 5.2.4 --- Building The Test Bench --- p.113 / Chapter 5.2.5 --- Disturbance In IMU Data --- p.113 / Chapter 5.2.6 --- The Solution To IMU Saturation --- p.115 / Chapter 6 --- Conclusion --- p.118 / Chapter 6.1 --- Actuation Dynamics --- p.118 / Chapter 6.2 --- State Estimation for GNSS Outage --- p.119 / Chapter 6.3 --- Hierarchical PD Controller --- p.121 / Chapter A --- Appendix - Derivation From Recursive Least Square Estimation To Kalman Filter --- p.122 / Chapter A.1 --- Recursive Least Square --- p.122 / Chapter A.1.1 --- Alternate Estimator form for RLS --- p.134 / Chapter A.1.2 --- Propagation of States and Covariance --- p.137 / Chapter A.1.3 --- Kalman Filter --- p.139 / Chapter B --- Appendix - Actuation by Gyroscopic Effect --- p.144 / Chapter B.1 --- Expression of The Induced Moment Due to Gyroscopic Effect In The Total External Moment --- p.150 / Chapter B.2 --- An Illustrated Example --- p.153 / Chapter B.3 --- Another Derivation By Using A Different Orientation Definition --- p.156 / Chapter B.4 --- Dimensions of the helicopter for experiments --- p.166 / Bibliography --- p.167

Chen, Yipin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 81-83). / Abstracts in English and Chinese. / Abstract --- p.1 / 摘要 --- p.2 / Table of Contents --- p.3 / Acknowledgements --- p.4 / Nomenclature --- p.5 / List of Figures --- p.9 / Chapter 1 --- Introduction / Chapter 1.1 --- Motivation and Literature Review --- p.11 / Chapter 1.2 --- Background --- p.13 / Chapter 1.3 --- Research Overview --- p.14 / Chapter 1.4 --- Thesis Outline --- p.15 / Chapter 2 --- Kinematic and Dynamic Modeling / Chapter 2.1 --- Helicopter Dynamics --- p.16 / Chapter 2.2 --- Kinematics of Point Feature Projection --- p.19 / Chapter 2.3 --- Kinematics of Line Feature Projection --- p.22 / Chapter 3 --- Adaptive Visual Servoing with Uncalibrated Camera / Chapter 3.1 --- On-line Parameter Estimation --- p.25 / Chapter 3.2 --- Controller Design --- p.28 / Chapter 3.3 --- Stability Analysis --- p.30 / Chapter 3.4 --- Simulation --- p.33 / Chapter 4 --- Adaptive Control with Unknown IMU Position / Chapter 4.1 --- Control Strategies --- p.47 / Chapter 4.1.1 --- Dynamic Model with Rotor Dynamics --- p.47 / Chapter 4.1.2 --- p.50 / Chapter 4.2 --- Stability Analysis --- p.55 / Chapter 4.3 --- Simulation --- p.57 / Chapter 5 --- Conclusions / Chapter 5.1 --- Summary --- p.64 / Chapter 5.2 --- Contributions --- p.65 / Chapter 5.3 --- Future Research --- p.65 / Chapter A --- Inertial Matrix of the Helicopter --- p.66 / Chapter B --- Induced Torque --- p.69 / Chapter C --- Unknown Parameter Vectors and Initial Estimation Values --- p.72 / Chapter D --- Cauchy Inequality --- p.74 / Chapter E --- Rotor Dynamics --- p.77 / Bibliography --- p.81

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