In classical model reference adaptive control, the goal is to design a controller to make the closed-loop system act like a prespecified stable reference model. A recent approach yields a linear periodic controller which simultaneously performs probing, estimation, and control. This linear controller is not only able to handle time-varying systems, but also provides exponential stability. In addition, from simulations, it is found that the controller has excellent noise rejection in certain cases. In this thesis, we used the induced noise gain as the measurement of noise rejection. For plants that are minimum phase with relative degree one, we started with the case where the plant is first order and linear time-invariant. Then we moved to the case where the plant is first order and linear time-varying. Finally, we extended to the general case where the plant is linear time-varying with relative degree one. For the above cases, we quantitatively investigated how certain control parameters affect the induced noise gain.

Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in simulation, a fictitious actuator model is developed that fits experimentally observed characteristics of flow control actuators in static flight conditions as well as possible coupling effects between actuation, the dynamics of flow field, and the rigid body dynamics of the vehicle.

In this thesis, an image-based uncalibrated visual servo is proposed for image tracking tasks in highly disturbed environment, such as a remotely operated vehicle performing observing or investigation objects under the influence of undersea current. For the conditions that the target model and the camera parameters are unknown, the control framework applies the scale invariant feature transform (SIFT) to extract image features. Furthermore, a robust adaptive control law is implemented to overcome the effect caused by camera calibration parameters. Then by using three different types of camera¡¦s motion: pan, tilt, and zoom to maintain the target always at the central position on the image plane.

Based on Lyapunov theorem, two different types of control schemes for solving robust tracking problems are presented in this thesis. The first one is model reference adaptive sliding mode control, which is designed for a class of SISO LTI systems with relative degree one possessing additive and multiplicative unstructured uncertainties in the input and output channels. By introducing a perturbation estimation process embedded in the proposed control scheme, the chattering phenomenon can be reduced effectively since only the perturbation estimation error needs to be overcomed. The second one is optimal model reference adaptive control, which is designed for a class of multi-input systems with input non-linearity. These systems are subject to model uncertainties and time-varying delay.

Based on the Lyapunov stability theorem, an optimal model reference adaptive control (OMRAC) scheme with perturbation estimation is presented in this thesis to solve robust tracking problems. The plant considered belongs to a class of MIMO perturbed dynamic systems with input nonlinearity and time varying delay in the state. The proposed control scheme contains three types of controllers. The first one is a linear feedback controller, which is an optimal controller if there is no perturbation. The second one is an adaptive controller, it is used for adapting the unknown upper bound of perturbation estimation error. The last one is the perturbation estimation mechanism. The property of uniformly ultimately boundness is proved under the proposed control scheme, and the effects of each design parameter on the dynamic performance is analyzed. Two numerical examples are given for demonstrating the feasibility of the proposed methodology.

Based on the Lyapunov stability theorem, a model reference adaptive variable structure control (MRAVSC) scheme with perturbation estimation is presented in this thesis for solving robust tracking problems. The plant considered belongs to a class of MIMO linear time invariant systems with arbitrary relative degree possessing additive and multiplicative unstructured uncertainties in the input and output channels. By introducing a perturbation estimation process embedded in the proposed control scheme, both the perturbations and differentials of tracking errors can be estimated. In addition, the proposed control scheme also contains an adaptive mechanism in order to automatically adapt the unknown upper bound of perturbation estimation error, and guarantee the property of uniformly ultimate boundedness for the closed-loop controlled system. Finally, two numerical examples are presented to demonstrate the feasibility of the proposed control scheme.

Thesis (M.S.)--California Polytechnic State University, 2009. / Mode of access: Internet. Title from PDF title page; viewed on Feb. 10, 2010. Major professor: Dr. Eric Mehiel. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree of Master of Science in Aerospace Engineering." "October, 2009." Includes bibliographical references (p. 55-56).

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 92-93).

Thesis (Ph. D.)--Ohio State University, 2009. / Title from first page of PDF file. Includes vita. Includes bibliographical references (p. 84-89).

This dissertation considers the general problem of controlling dynamic systems subject to large-scale sensor and actuator uncertainties. The assumption is made that the uncertainty is limited to either pure rotation (i.e. special orthogonal matrix) or that each axis is rotated independently. Although uncertainty can appear in more general forms, this representation describes a ``net-effect'' when the ideal axes have become misaligned that is of fundamental importance to the control of numerous systems. Adaptive observers and controllers are introduced that guarantee perfect reference trajectory tracking even with the appearance of these large-scale uncertainties. The specific contributions of this dissertation are as follows: (I) the problem of rigid-body attitude tracking with vector measurements, unknown gyro bias, and unknown body inertia matrix is addressed for the first time. In this problem, the body attitude acts as unknown special orthogonal matrix (i.e. sensor uncertainty). A set of adaptive observers and an adaptive controller is presented that guarantees perfect tracking as well as convergence of the attitude and bias estimates through a Lyapunov stability analysis. (II) An adaptive observer is developed for the scenario where the control is pre-multiplied by an unknown constant scaling and rotation matrix which gives a non-affine representation of the uncertainty. The observer is shown to be convergent given a certain persistence of excitation condition on the input signal and using a smooth projection scheme on the estimate of the unknown scaling. In addition, the observer is combined with a stabilizing control to guarantee perfect tracking which establishes a separation like property. (III) The class of uncertainties where each axis of the control is independently misaligned is examined. The problem is split into studies of in-plane and out-of-plane misalignment angles given that they exhibit fundamental technical differences in establishing convergence. Where possible, rigorous stability proofs are given for a series of adaptive observers. The structure of the observers assure that the estimates do not introduce any singularities into the control problem other than those inherent from the misalignment geometry. The inherent singularities are avoided through the use of projection schemes which allow for extension to the control problem. This work represents the first significant effort to develop adaptive observers and controllers for this class of misalignments. / text