The design and validation of a low-level backstepping controller for speed and heading that is adaptive in speed for a twin-hulled underactuated unmanned surface vessel is presented. Consideration is given to the autonomous launch and recovery of an underwater vehicle in the decision to pursue an adaptive control approach. Basic system identification is conducted and numerical simulation of the vessel is developed and validated. A speed and heading controller derived using the backstepping method and a model reference adaptive controller are developed and ultimately compared through experimental testing against a previously developed control law. Experimental tests show that the adaptive speed control law outperforms the non-adaptive alternatives by as much as 98% in some cases; however heading control is slightly sacrificed when using the adaptive speed approach. It is found that the adaptive control law is the best alternative when drag and mass properties of the vessel are time-varying and uncertain. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

novel approach to extend the decision-making capabilities of unmanned surface vehicles (USVs) is presented in this work. A multi-objective framework is described where separate controllers command different behaviors according to a desired trajectory. Three behaviors are examined – transiting, station-keeping and reversing. Given the desired trajectory, the vehicle is able to autonomously recognize which behavior best suits a portion of the trajectory. The USV uses a combination of a supervisory switching control structure and a reinforcement learning algorithm to create a hybrid deliberative and reactive approach to switch between controllers and actions. Reinforcement learning provides a deliberative method to create a controller switching policy, while supervisory switching control acts reactively to instantaneous changes in the environment. Each action is restricted to one controller. Due to the nonlinear effects in these behaviors, two underactuated backstepping controllers and a fully-actuated backstepping controller are proposed for each transiting, reversing and station-keeping behavior, respectively, restricted to three degrees of freedom. Field experiments are presented to validate this system on the water with a physical USV platform under Sea State 1 conditions. Main outcomes of this work are that the proposed system provides better performance than a comparable gain-scheduled nonlinear controller in terms of an Integral of Absolute Error metric. Additionally, the deliberative component allows the system to identify dynamically infeasible trajectories and properly accommodate them. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Systems with time delays have a broad range of applications not only in control systems but also in many other disciplines such as mathematical biology, financial economics, etc. The time delays cause more complex behaviours of the systems. It requires more sophisticated analysis due to the infinite dimensional structure of the space spaces. In this thesis we investigate stability properties associated with output functions of delay systems. Our primary target is the equivalent Lyapunov characterization of input-tooutput stability (ios). A main approach used in this work is the Lyapuno Krasovskii functional method. The Lyapunov characterization of the so called output-Lagrange stability is technically the backbone of this work, as it induces a Lyapunov description for all the other output stability properties, in particular for ios. In the study, we consider two types of output functions. The first type is defined in between Banach spaces, whereas the second type is defined between Euclidean spaces. The Lyapunov characterization for the first type of output maps provides equivalence between the stability properties and the existence of the Lyapunov-Krasovskii functionals. On the other hand, as a special case of the first type, the second type output renders flexible Lyapunov descriptions that are more efficient in applications. In the special case when the output variables represent the complete collection of the state variables, our Lyapunov work lead to Lyapunov characterizations of iss, complementing the current iss theory with some novel results. We also aim at understanding how output stability are affected by the initial data and the external signals. Since the output variables are in general not a full collection of the state variables, the overshoots and decay properties may be affected in different ways by the initial data of either the state variables or just only the output variables. Accordingly, there are different ways of defining notions on output stability, making them mathematically precisely. After presenting the definitions, we explore the connections of these notions. Understanding the relation among the notions is not only mathematically necessary, it also provides guidelines in system control and design. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

In this research, a wind feedforward (FF) controller has been developed to augment closed loop feedback controllers for the position and heading station keeping control of Unmanned Surface Vehicles (USVs). The performance of the controllers was experimentally tested using a 16 foot USV in an outdoor marine environment. The FF controller was combined with three nonlinear feedback controllers, a Proportional–Derivative (PD) controller, a Backstepping (BS) controller, and a Sliding mode (SM) controller, to improve the station-keeping performance of the USV. To address the problem of wind model uncertainties, adaptive wind feedforward (AFF) control schemes are also applied to the FF controller, and implemented together with the BS and SM feedback controllers. The adaptive law is derived using Lyapunov Theory to ensure stability. On-water station keeping tests of each combination of FF and feedback controllers were conducted in the U.S. Intracoastal Waterway in Dania Beach, FL USA. Five runs of each test condition were performed; each run lasted at least 10 minutes. The experiments were conducted in Sea State 1 with an average wind speed of between 1 to 4 meters per second and significant wave heights of less than 0.2 meters. When the performance of the controllers is compared using the Integral of the Absolute Error (IAE) of position criterion, the experimental results indicate that the BS and SM feedback controllers significantly outperform the PD feedback controller (e.g. a 33% and a 44% decreases in the IAE, respectively). It is also found that FF is beneficial for all three feedback controllers and that AFF can further improve the station keeping performance. For example, a BS feedback control combined with AFF control reduces the IAE by 25% when compared with a BS feedback controller combined with a non-adaptive FF controller. Among the eight combinations of controllers tested, SM feedback control combined with AFF control gives the best station keeping performance with an average position and heading error of 0.32 meters and 4.76 degrees, respectively. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

by Ho King Choi. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1991. / Includes bibliographical references. / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Adaptive Filter and Adaptive System --- p.1 / Chapter 1.2 --- Applications of Adaptive Filter --- p.4 / Chapter 1.2.1 --- System Identification --- p.4 / Chapter 1.2.2 --- Noise Cancellation --- p.6 / Chapter 1.2.3 --- Echo Cancellation --- p.8 / Chapter 1.2.4 --- Speech Processing --- p.10 / Chapter 1.3 --- Chapter Summary --- p.14 / References --- p.15 / Chapter 2. --- Adaptive Filter Structures and Algorithms --- p.17 / Chapter 2.1 --- Filter Structures for Adaptive Filtering --- p.17 / Chapter 2.2 --- Adaptation Algorithms --- p.22 / Chapter 2.2.1 --- The LMS Adaptation Algorithm --- p.24 / Chapter 2.2.1.1 --- Convergence Analysis --- p.28 / Chapter 2.2.1.2 --- Steady State Performance --- p.33 / Chapter 2.2.2 --- The RLS Adaptation Algorithm --- p.35 / Chapter 2.3 --- Chapter Summary --- p.39 / References --- p.41 / Chapter 3. --- Parallel Split Adaptive System --- p.45 / Chapter 3.1 --- Parallel Form Adaptive Filter --- p.45 / Chapter 3.2 --- Joint Process Estimation with a Split-Path Adaptive Filter --- p.49 / Chapter 3.2.1 --- The New Adaptive System Identification Configuration --- p.53 / Chapter 3.2.2 --- Analysis of the Split-Path System Modeling Structure --- p.57 / Chapter 3.2.3 --- Comparison with the Non-Split Configuration --- p.63 / Chapter 3.2.4 --- Some Notes on Even Filter Order Case --- p.67 / Chapter 3.2.5 --- Simulation Results --- p.70 / Chapter 3.3 --- Autoregressive Modeling with a Split-Path Adaptive Filter --- p.75 / Chapter 3.3.1 --- The Split-Path Adaptive Filter for AR Modeling --- p.79 / Chapter 3.3.2 --- Analysis of the Split-Path AR Modeling Structure --- p.84 / Chapter 3.3.3 --- Comparison with Traditional AR Modeling System --- p.89 / Chapter 3.3.4 --- Selection of Step Sizes --- p.90 / Chapter 3.3.5 --- Some Notes on Odd Filter Order Case --- p.94 / Chapter 3.3.6 --- Simulation Results --- p.94 / Chapter 3.3.7 --- Application to Noise Cancellation --- p.99 / Chapter 3.4 --- Chapter Summary --- p.107 / References --- p.109 / Chapter 4. --- Serial Split Adaptive System --- p.112 / Chapter 4.1 --- Serial Form Adaptive Filter --- p.112 / Chapter 4.2 --- Time Delay Estimation with a Serial Split Adaptive Filter --- p.125 / Chapter 4.2.1 --- Adaptive TDE --- p.125 / Chapter 4.2.2 --- Split Filter Approach to Adaptive TDE --- p.132 / Chapter 4.2.3 --- Analysis of the New TDE System --- p.136 / Chapter 4.2.3.1 --- Least-mean-square Solution --- p.138 / Chapter 4.2.3.2 --- Adaptation Algorithm and Performance Evaluation --- p.142 / Chapter 4.2.4 --- Comparison with Traditional Adaptive TDE Method --- p.147 / Chapter 4.2.5 --- System Implementation --- p.148 / Chapter 4.2.6 --- Simulation Results --- p.148 / Chapter 4.2.7 --- Constrained Adaptation for the New TDE System --- p.156 / Chapter 4.3 --- Chapter Summary --- p.163 / References --- p.165 / Chapter 5. --- Extension of the Split Adaptive Systems --- p.167 / Chapter 5.1 --- The Generalized Parallel Split System --- p.167 / Chapter 5.2 --- The Generalized Serial Split System --- p.170 / Chapter 5.3 --- Comparison between the Parallel and the Serial Split Adaptive System --- p.172 / Chapter 5.4 --- Integration of the Two Forms of Split Predictors --- p.177 / Chapter 5.5 --- Application of the Integrated Split Model to Speech Encoding --- p.179 / Chapter 5.6 --- Chapter Summary --- p.188 / References --- p.139 / Chapter 6. --- Conclusions --- p.191 / References --- p.197

Thesis. 1978. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Includes bibliographical references. / by Richard Tse-min Ku. / Ph.D.

Thesis. 1975. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Bibliography: leaves 258-259. / by Christopher S. Greene. / M.S.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Prashant Parikh. / M.S.

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 110-113. / M.S.