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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Thesis (Ph.D.)--University of Wyoming, 2008. / Title from PDF title page (viewed on Apr. 2, 2010). Includes bibliographical references (p. 127-135).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1970. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

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Connectionism is an approach currently being used in the field of cognitive science to investigate intelligence. Connectionist models are based upon the information processing properties of neurons in the brain and consist of very many, simple, interconnected processing elements which operate upon simple signals in parallel. The main objective of the work reported here is to show that connectionism may be applied to areas other than cognitive science. A simulation program has been implemented in which a connectionist network performs the real-time planning and control activities required to supervise the movement and processing of parts in a manufacturing system. The concurrency of connectionism is exploited in such a way that production of a part type may be characterised as “parallel”, where many machine tools are specified to perform each one of the operations required to transform raw material into finished product. The connectionist network is able to control all of these machines simultaneously and in real-time so that many parts can be at any stage of completion in a production facility. The precise routing of a part through the production facility is not specified in advance. Instead, the machine to which a part is scheduled next and the route by which it reaches this machine are decided when the part completes its current operation. These decisions are based upon the availabilities of machines at the time the decisions are made. The connectionist network is able to make these decisions, for every part in the production facility, in negligible time. The benefit of this approach is shown when the breakdown of machines is simulated. The network is able to react autonomously to breakdown by scheduling and routing parts around the affected machine. The necessary computations can be performed in real-time so that breakdown does not cause the manufacturing system to halt while production is re-planned.