by John Tak Kuen Koo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 217-[220]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- "Robust, Adaptive and Fuzzy Control" --- p.2 / Chapter 1.3 --- Adaptive Fuzzy Control --- p.4 / Chapter 1.4 --- Object of Study --- p.10 / Chapter 1.5 --- Scope of the Thesis --- p.13 / Chapter 2 --- Background on Adaptive Control and Fuzzy Logic Control --- p.17 / Chapter 2.1 --- Adaptive control --- p.17 / Chapter 2.1.1 --- Model reference adaptive systems --- p.20 / Chapter 2.1.2 --- MIT Rule --- p.23 / Chapter 2.1.3 --- Model Reference Adaptive Control (MRAC) --- p.24 / Chapter 2.2 --- Fuzzy Logic Control --- p.33 / Chapter 2.2.1 --- Fuzzy sets and logic --- p.33 / Chapter 2.2.2 --- Fuzzy Relation --- p.40 / Chapter 2.2.3 --- Inference Mechanisms --- p.43 / Chapter 2.2.4 --- Defuzzification --- p.49 / Chapter 3 --- Explicit Form of a Class of Fuzzy Logic Controllers --- p.51 / Chapter 3.1 --- Introduction --- p.51 / Chapter 3.2 --- Construction of a class of fuzzy controller --- p.53 / Chapter 3.3 --- Explicit form of the fuzzy controller --- p.57 / Chapter 3.4 --- Design criteria on the fuzzy controller --- p.65 / Chapter 3.5 --- B-Spline fuzzy controller --- p.68 / Chapter 4 --- Model Reference Adaptive Fuzzy Control (MRAFC) --- p.73 / Chapter 4.1 --- Introduction --- p.73 / Chapter 4.2 --- "Fuzzy Controller, Plant and Reference Model" --- p.75 / Chapter 4.3 --- Derivation of the MRAFC adaptive laws --- p.79 / Chapter 4.4 --- "Extension to the Multi-Input, Multi-Output Case" --- p.84 / Chapter 4.5 --- Simulation --- p.90 / Chapter 5 --- MRAFC on a Class of Nonlinear Systems: Type I --- p.97 / Chapter 5.1 --- Introduction --- p.98 / Chapter 5.2 --- Choice of Controller --- p.99 / Chapter 5.3 --- Derivation of the MRAFC adaptive laws --- p.102 / Chapter 5.4 --- Example: Stabilization of a pendulum --- p.109 / Chapter 6 --- MRAFC on a Class of Nonlinear Systems: Type II --- p.112 / Chapter 6.1 --- Introduction --- p.113 / Chapter 6.2 --- Fuzzy System as Function Approximator --- p.114 / Chapter 6.3 --- Construction of MRAFC for the nonlinear systems --- p.118 / Chapter 6.4 --- Input-Output Linearization --- p.130 / Chapter 6.5 --- MRAFC with Input-Output Linearization --- p.132 / Chapter 6.6 --- Example --- p.136 / Chapter 7 --- Analysis of MRAFC System --- p.140 / Chapter 7.1 --- Averaging technique --- p.140 / Chapter 7.2 --- Parameter convergence --- p.143 / Chapter 7.3 --- Robustness --- p.152 / Chapter 7.4 --- Simulation --- p.157 / Chapter 8 --- Application of MRAFC scheme on Manipulator Control --- p.166 / Chapter 8.1 --- Introduction --- p.166 / Chapter 8.2 --- Robot Manipulator Control --- p.170 / Chapter 8.3 --- MRAFC on Robot Manipulator Control --- p.173 / Chapter 8.3.1 --- Part A: Nonlinear-function feedback fuzzy controller --- p.174 / Chapter 8.3.2 --- Part B: State-feedback fuzzy controller --- p.182 / Chapter 8.4 --- Simulation --- p.186 / Chapter 9 --- Conclusion --- p.199 / Chapter A --- Implementation of MRAFC Scheme with Practical Issues --- p.203 / Chapter A.1 --- Rule Generation by MRAFC scheme --- p.203 / Chapter A.2 --- Implementation Considerations --- p.211 / Chapter A.3 --- MRAFC System Design Procedure --- p.215 / Bibliography --- p.217

by Wan, Kwok Fai. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 138-148). / Chapter Chapter 1. --- Adaptive signal processing and its applications --- p.1 / Chapter 1.1. --- Introduction --- p.1 / Chapter 1.2. --- Applications of adaptive system --- p.3 / Chapter 1.2.1. --- Adaptive noise cancellation --- p.3 / Chapter 1.2.2. --- Adaptive echo cancellation --- p.5 / Chapter 1.2.3. --- Adaptive line enhancement --- p.5 / Chapter 1.2.4. --- Adaptive linear prediction --- p.7 / Chapter 1.2.5. --- Adaptive system identification --- p.8 / Chapter 1.3. --- Algorithms for adaptive systems --- p.10 / Chapter 1.4. --- Transform domain adaptive filtering --- p.12 / Chapter 1.5 --- The motivation and organization of the thesis --- p.13 / Chapter Chapter 2. --- Time domain split-path adaptive filter --- p.16 / Chapter 2.1. --- Adaptive transversal filter and the LMS algorithm --- p.17 / Chapter 2.1.1. --- Wiener-Hopf solution --- p.17 / Chapter 2.1.2. --- The LMS adaptive algorithm --- p.20 / Chapter 2.2. --- Split structure adaptive filtering --- p.23 / Chapter 2.2.1. --- Split structure of an adaptive filter --- p.24 / Chapter 2.2.2. --- Split-path structure for a non-symmetric adaptive filter --- p.25 / Chapter 2.3. --- Split-path adaptive median filtering --- p.29 / Chapter 2.3.1. --- Median filtering and median LMS algorithm --- p.29 / Chapter 2.3.2. --- The split-path median LMS (SPMLMS) algorithm --- p.32 / Chapter 2.3.3. --- Convergence analysis of SPMLMS --- p.36 / Chapter 2.4. --- Computer simulation examples --- p.41 / Chapter 2.5. --- Summary --- p.45 / Chapter Chapter 3. --- Multi-stage split structure adaptive filtering --- p.46 / Chapter 3.1. --- Introduction --- p.46 / Chapter 3.2. --- Split structure for a symmetric or an anti-symmetric adaptive filter --- p.48 / Chapter 3.3. --- Multi-stage split structure for an FIR adaptive filter --- p.56 / Chapter 3.4. --- Properties of the split structure LMS algorithm --- p.59 / Chapter 3.5. --- Full split-path adaptive algorithm for system identification --- p.66 / Chapter 3.6. --- Summary --- p.71 / Chapter Chapter 4. --- Transform domain split-path adaptive algorithms --- p.72 / Chapter 4.1. --- Introduction --- p.73 / Chapter 4.2. --- general description of transforms --- p.74 / Chapter 4.2.1. --- Fast Karhunen-Loeve transform --- p.75 / Chapter 4.2.2. --- Symmetric cosine transform --- p.77 / Chapter 4.2.3. --- Discrete sine transform --- p.77 / Chapter 4.2.4. --- Discrete cosine transform --- p.78 / Chapter 4.2.5. --- Discrete Hartley transform --- p.78 / Chapter 4.2.6. --- Discrete Walsh transform --- p.79 / Chapter 4.3. --- Transform domain adaptive filters --- p.80 / Chapter 4.3.1. --- Structure of transform domain adaptive filters --- p.80 / Chapter 4.3.2. --- Properties of transform domain adaptive filters --- p.83 / Chapter 4.4. --- Transform domain split-path LMS adaptive predictor --- p.84 / Chapter 4.5. --- Performance analysis of the TRSPAF --- p.93 / Chapter 4.5.1. --- Optimum Wiener solution --- p.93 / Chapter 4.5.2. --- Steady state MSE and convergence speed --- p.94 / Chapter 4.6. --- Computer simulation examples --- p.96 / Chapter 4.7. --- Summary --- p.100 / Chapter Chapter 5. --- Tracking optimal convergence factor for transform domain split-path adaptive algorithm --- p.101 / Chapter 5.1. --- Introduction --- p.102 / Chapter 5.2. --- The optimal convergence factors of TRSPAF --- p.104 / Chapter 5.3. --- Tracking optimal convergence factors for TRSPAF --- p.110 / Chapter 5.3.1. --- Tracking optimal convergence factor for gradient-based algorithms --- p.111 / Chapter 5.3.2. --- Tracking optimal convergence factors for LMS algorithm --- p.112 / Chapter 5.4. --- Comparison of optimal convergence factor tracking method with self-orthogonalizing method --- p.114 / Chapter 5.5. --- Computer simulation results --- p.116 / Chapter 5.6. --- Summary --- p.121 / Chapter Chapter 6. --- A unification between split-path adaptive filtering and discrete Walsh transform adaptation --- p.122 / Chapter 6.1. --- Introduction --- p.122 / Chapter 6.2. --- A new ordering of the Walsh functions --- p.124 / Chapter 6.3. --- Relationship between SM-ordered Walsh function and other Walsh functions --- p.126 / Chapter 6.4. --- Computer simulation results --- p.132 / Chapter 6.5. --- Summary --- p.134 / Chapter Chapter 7. --- Conclusion --- p.135 / References --- p.138

Ho Wang Hei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 93-94). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Contributions --- p.1 / Chapter 1.1.1 --- Scalability of Network Capacity with Power Control --- p.1 / Chapter 1.1.2 --- Trade-off between network capacity and fairness with Power Control --- p.3 / Chapter 1.2 --- Related Work --- p.4 / Chapter 1.3 --- Organization of the Thesis --- p.6 / Chapter Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Hidden- and Exposed-node Problems --- p.8 / Chapter 2.1.1 --- HN-free Design (HFD) --- p.9 / Chapter 2.1.2 --- Non-Scalable Capacity in 802.11 caused by EN --- p.11 / Chapter 2.2 --- Shortcomings of Minimum-Transmit-Power Approach --- p.13 / Chapter Chapter 3 --- Simultaneous Transmissions Constraints with Power Control --- p.15 / Chapter 3.1 --- Physical-Collision Constraints --- p.16 / Chapter 3.1.1 --- Protocol-Independent Physical-Collision Constraints --- p.17 / Chapter 3.1.2 --- Protocol-Specific Physical-Collision Constraints --- p.17 / Chapter 3.2 --- Protocol-Collision-Prevention Constraints --- p.18 / Chapter 3.2.1 --- Transmitter-Side Carrier-Sensing Constraints --- p.18 / Chapter 3.2.2 --- Receiver-Side Carrier-Sensing Constraints --- p.19 / Chapter Chapter 4 --- Graph Models for Capturing Transmission Constraints and Hidden-node Problems --- p.20 / Chapter 4.1 --- Link-Interference Graph from Physical-Collision Constraints --- p.21 / Chapter 4.2 --- Protocol-Collision-Prevention Graphs --- p.22 / Chapter 4.3 --- Ideal Protocol-Collision-Prevention Graphs --- p.22 / Chapter 4.4 --- Definition of HN and EN and their Investigation using Graph Model --- p.23 / Chapter 4.5 --- Attacking Cases --- p.26 / Chapter Chapter 5 --- Scalability of Network Capacity with Adaptive Power Control --- p.27 / Chapter 5.1 --- Selective Disregard of NAVs (SDN) --- p.27 / Chapter 5.2 --- Scalability of Network Capacity: Analytical Discussion --- p.29 / Chapter 5.3 --- Adaptive Power Control for SDN --- p.31 / Chapter 5.3.1 --- Per-iteration Power Adjustment --- p.32 / Chapter 5.3.2 --- Power Control Scheduling Strategy --- p.35 / Chapter 5.3.3 --- Power Exchange Algorithm --- p.39 / Chapter 5.3.4 --- Comparison of Scheduling Strategies --- p.41 / Chapter 5.4 --- Scalability of Network Capacity: Numerical Results --- p.43 / Chapter Chapter 6 --- Decoupled Adaptive Power Control (DAPC) --- p.45 / Chapter 6.1 --- Per-iteration Power Adjustment --- p.45 / Chapter 6.2 --- Power Exchange Algorithm --- p.47 / Chapter 6.3 --- Implementation of DAPC --- p.48 / Chapter 6.4 --- Deadlock Problem in DAPC --- p.50 / Chapter Chapter 7 --- Progressive-Uniformly-Scaled Power Control (PUSPC): Deadlock-free Design --- p.53 / Chapter 7.1 --- Algorithm of PUSPC --- p.53 / Chapter 7.2 --- Deadlock-free property of PUSPC --- p.60 / Chapter 7.3 --- Deadlock Resolution of DAPC using PUSPC --- p.62 / Chapter Chapter 8 --- Incremental Power Adaptation --- p.65 / Chapter 8.1 --- Incremental Power Adaptation (IPA) --- p.65 / Chapter 8.2 --- Maximum Allowable Power in EPA --- p.68 / Chapter 8.3 --- Numerical Results of IPA --- p.71 / Chapter Chapter 9 --- Numerical Results and the Trade-off between EN and HN --- p.78 / Chapter Chapter 10 --- Conclusion --- p.83 / Appendix I: Proof of the Correct Operation of PE Algorithm for APC for SDN --- p.86 / Appendix II: Proof of the Correct Operation of PE Algorithm for DAPC --- p.89 / Appendix III: Scalability of the Communication Cost of PE Algorithm --- p.91 / Bibliography --- p.93

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Charles Edward Rohrs. / Ph.D.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Henry Rolan Shomber. / M.S.

Repetitive Control (RC) seeks to converge to zero tracking error of a feedback control system performing periodic command as time progresses, or to cancel the influence of a periodic disturbance as time progresses, by observing the error in the previous period. Iterative Learning Control (ILC) is similar, it aims to converge to zero tracking error of system repeatedly performing the same task, and also adjusting the command to the feedback controller each repetition based on the error in the previous repetition. Compared to the conventional feedback control design methods, RC and ILC improve the performance over repetitions, and both aiming at zero tracking error in the real world instead of in a mathematical model. Linear Model Predictive Control (LMPC) normally does not aim for zero tracking error following a desired trajectory, but aims to minimize a quadratic cost function to the prediction horizon, and then apply the first control action. Then repeat the process each time step. The usual quadratic cost is a trade-off function between tracking accuracy and control effort and hence is not asking for zero error. It is also not specialized to periodic command or periodic disturbance as RC is, but does require that one knows the future desired command up to the prediction horizon. The objective of this dissertation is to present various design schemes of improving the tracking performance in a control system based on ILC, RC and LMPC. The dissertation contains four major chapters. The first chapter studies the optimization of the design parameters, in particular as related to measurement noise, and the need of a cutoff filter when dealing with actuator limitations, robustness to model error. The results aim to guide the user in tuning the design parameters available when creating a repetitive control system. In the second chapter, we investigate how ILC laws can be converted for use in RC to improve performance. And robustification by adding control penalty in cost function is compared to use a frequency cutoff filter. The third chapter develops a method to create desired trajectories with a zero tracking interval without involving an unstable inverse solution. An easily implementable feedback version is created to optimize the same cost every time step from the current measured position. An ILC algorithm is also created to iteratively learn to give local zero error in the real world while using an imperfect model. This approach also gives a method to apply ILC to endpoint problem without specifying an arbitrary trajectory to follow to reach the endpoint. This creates a method for ILC to apply to such problems without asking for accurate tracking of a somewhat arbitrary trajectory to accomplish learning to reach the desired endpoint. The last chapter outlines a set of uses for a stable inverse in control applications, including Linear Model Predictive Control (LMPC), and LMPC applied to Repetitive Control (RC-LMPC), and a generalized form of a one-step ahead control. An important characteristic is that this approach has the property of converging to zero tracking error in a small number of time steps, which is finite time convergence instead of asymptotic convergence as time tends to infinity.

Finally, we study a typical heterogeneous network, Wireless Biomedical Sensor Network (WBSN), as it consists of various types of biosensors to monitor different physiological parameters. WBSN will help to enhance medical services with its unique advantages in long-term monitoring, easy network deployment, wireless connections, and ambulatory capabilities. (Abstract shortened by UMI.) / Secondly, for the purpose of providing geographical information for the topology management, we investigate the problem of power adaptive localization based on received signal strength (RSS), aiming at tackling the problem of inconsistent signal strength observation caused by tuning power levels. We propose a localization algorithm based on the particle filtering technique for sensor networks assisted by multiple transmission power levels. As a result, the novel contribution in this part is to intelligently incorporate changing transmission power levels into the particle filtering process as dynamic evidences and make an accurate localization. The proposed particle filtering technique based localization algorithm effectively circumvents the inconsistent observations under different power settings. It picks up the information of RSS from the beacons or the neighboring nodes to infer position information, without requiring additional instrumentation. We then evaluate the power adaptive localization algorithm via simulation studies and the results indicate that the proposed algorithm outperforms the algorithm of iterative least-square estimation, which does not utilize multiple power levels. In addition, we proposed a particle-filtering localization based on the acoustic asymmetric patterns of the acoustic sensors. As a result, the proposed particle filter based localization algorithms can facilitate the topology management in heterogeneous sensor networks. / We start by formulating the problem of topology optimization in the context of game theory and then analyze the equilibrium resulted from the decentralized interactions between the heterogeneous sensors. Majority of the existing topology control approaches require a centralized controller to obtain a global network graph and formulate the issue as a problem of transmission range assignment. The centralized algorithms are inapplicable for large-scale sensor networks due to the heavy communication overhead. In addition, these algorithms rarely consider the cross-layer consequences of the power adjustment, such as the quality of received signals at physical layer, the network connectivity, and the spatial reuse at network layer. Considering the aforementioned cross-layer interactive effects caused by power scheduling, we study the utility function that balances the physical layer link quality characterized by the frame success rate and the network layer robustness characterized by K-connectivity, while minimizing the power consumption. We prove the existence of the Nash equilibrium for complete-information game formulation. Because the heterogeneous sensors typically react to neighboring environment based on local information and the states of sensors are evolving over time, the power-scheduling problem in WHSN is further formulated into a more realistic incomplete-information dynamic game model. We then analyze the separating equilibrium, one of the perfect Bayesian equilibriums resulted from the dynamic game, with the sensors revealing their operational states from the actions. The sufficient and necessary conditions of the separating equilibrium existence are derived for the dynamic Bayesian game, which provide theoretical basis to the proposed power scheduling algorithms. / Wireless Heterogeneous Sensor Network (WHSN) is constructed from various sensor nodes with diverse capabilities in sensing units, transmission power levels, and energy resources, among a few others. The primary objective of the research reported in this thesis is to address the problem of power efficient topology optimization in WHSN, which is a much more complicated issue for network reliability, compared with homogeneous wireless sensor network (WSN). Two fundamental problems of topology management are addressed in this thesis: power scheduling based topology control and power adaptive localization. Distributed power scheduling offers an efficient way for the dynamic construction of network topology to meet its connectivity and reliability requirements. Power adaptive localization provides geographical information for topology management during the process of power adjustment. / Ren, Hongliang. / "February 2008." / Adviser: Qing-Hu Max Meng. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4944. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 140-157). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Wang Dan. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (p. 128-138). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Iterative Learning Control (ILC) iterates with a real world control system repeatedly performing the same task. It adjusts the control action based on error history from the previous iteration, aiming to converge to zero tracking error. ILC has been widely used in various applications due to its high precision in trajectory tracking, e.g. semiconductor manufacturing sensors that repeatedly perform scanning maneuvers. Designing effective feedback controllers for non-minimum phase (NMP) systems can be challenging. Applying Iterative Learning Control (ILC) to NMP systems is particularly problematic. Asking for zero error at sample times usually involves inverting the control system. However, the inverse process is unstable when the system has NMP zeros. The control action will grow exponentially every time step, and the error between time steps also grows exponentially. If there are NMP zeros on the negative real axis, the control action will alternate its sign every time step. ILC must be digital to use previous run data to improve the tracking error in the current run. There are two kinds of NMP digital systems, ones having intrinsic NMP zeros as images of continuous time NMP zeros, and NMP sampling zeros introduced by discretization. Two ILC design methods have been investigated in this thesis to handle NMP sampling zeros, producing zero tracking error at addressed sample times: (1) One can simply start asking for zero error after a few initial time steps, like using multiple zero order holds for the first addressed time step only (2) Or increase the sample rate, ask for zero error at the original rate, making two or more zero order holds per addressed time step. The internal instability can be manifested by the singular value decomposition of the input-output matrix. Non-minimum phase systems have particularly small singular values which are related to the NMP zeros. The aim is to eliminate these anomalous singular values. However, when applying the second approach, there are cases that the original anomalous singular values are gone, but some new anomalous singular values appear in the system matrix that cause difficulties to the inverse problem. Not asking for zero error for a small number of initial addressed time steps is shown to eliminate all anomalous singular values. This suggests that a more accurate statement of the second approach is: using multiple zero order holds per addressed time step, and eliminating a few initial addressed time steps if there are new anomalous singular values. We also extend the use of these methods to systems having intrinsic NMP zeros. By modifying ILC laws to perform pole-zero cancellation inside the unit circle, we observe that all of the rules for sampling zeros are effective for intrinsic zeros. Hence, one can now achieve convergence to zero tracking error at addressed time steps in ILC of NMP systems with a well behaved control action. In addition, this thesis studies the robustness of the two approaches along with several other candidate approaches with respect to model parameter uncertainty. Three classes of ILC laws are used. Both approaches show great robustness. Quadratic cost ILC is seen to have substantially better robustness to parameter uncertainty than the other laws.

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