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Approved for public release; distribution in unlimited. / This thesis considers the problem of time-optimal spacecraft slew maneuvers. Since the work of Bilimoria and Wie it has been known that the time-optimal reorientation of a symmetric rigid body was not the eigenaxis maneuver once thought to be correct. Here, this concept is extended to axisymmetric and asymmetric rigid body reorientations with idealized independent torque generating devices. The premise that the time-optimal maneuver is not, in general, an eigenaxis maneuver, is shown to hold for all spacecraft configurations. The methodology is then extended to include spacecraft control systems employing magnetic torque rods, a combination of pitch bias wheel with magnetic torque rods, and finally to control systems employing single gimbal control moment gyros. The resulting control solutions, designed within the limitations of the actuators, eliminate the requirement to avoid actuator singularities. Finally, by employing sampled-state feedback the viability of real-time optimal closed loop control is demonstrated.

Fung Wai-keung. / "February 5, 2001." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (p. 109-116). / 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.

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.

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

Vision is a flexible, non-contact sensor that can be used for position feedback in closed-loop control of dynamic systems. Current vision systems for industrial automation provide low sample rates and large sample delays relative to other types of position sensors. Poor sample rates and sample delays are a result of the vast volume of data that must be collected and processed by the vision system. A predictive visual tracker can help compensate for some of the deficiencies of current industrial vision systems. The objectives of the present research are to demonstrate that vision is a useful feedback sensor and prediction can be used to improve performance by compensating for the feedback delay of the vision system. An inverted pendulum was stabilized using a vision sensor as feedback to a state-feedback controller. The vision data was run through a d-step ahead predictor to compensate for the vision system delays. The system was simulated in Mat lab and an actual physical system was used to test the performance of the control system. The inverted pendulum provides a good test-bed for studying predictive control using vision feedback. The pendulum will fall without the constant adjustment of the cart position. The adjustment of the cart by the controller is delayed because of latency and quantization errors in vision feedback. The better the controller is able to compensate for delays and quantization errors, the greater its ability to stabilize the inverted pendulum. / Graduation date: 1996

This thesis presents an effective control design methodology using a one-step-ahead prediction adaptive control law and an adaptive control law based on a Lyapunov function. These control law were applied to a highly maneuverable high performance aircraft, in particular, a modified F/A-18. An adaptive controller is developed to maneuver an aircraft at a high angle of attack even if the aircraft is required to fly over a highly nonlinear flight regime. The adaptive controller presented in this thesis is based on linear, bilinear, and nonlinear prediction models with input constraints. It is shown that the linear, bilinear, and nonlinear adaptive controllers can be constructed to minimize the given cost function or Lyapunov function with respect to the control input at each step. The control is calculated such that the system follows the reference trajectory, and such that control signal remains within its constraints. From several simulation results, the nonlinear controller is controller is better than the linear controller. A nonlinear adaptive control law based on a Lyapunov function is designed such that control inputs are smoother than for the one-step-ahead prediction adaptive controller. / Graduation date: 1994

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