Dissertations / Theses on the topic 'Adaptive control'

This thesis describes discrete robust adaptive control methods based on using slow sampling and slow adaptation. For the stability analysis, we consider that the plant model order is not exactly known and assume that the estimation model order is lower than the plant model order. A stability condition is derived with a given upper limit for the adaptation gain which is related to a strictly positive real operator. Discussion of the relation between sampling and stability condition is then given. For the robust adaptive control design, we study slow adaptation and predictive control. For the slow adaptation, the main idea is to use only good estimates and use a compensation filter. Some frequency domain information on the plant is necessary for this method. For predictive control, we discuss the relationship between the extended control horizon and the critical sampling. For a simple case, it is shown that the larger extended control horizon brings more robust adaptive control. The purpose of this thesis is to provide robust discrete adaptive controller design guidelines, especially in such cases as using slow sampling frequency, slow adaptation rate. It is true that in practice, for various discrete adaptive control algorithms, slow sampling and slow adaptation rate will bring more robustness. The use of slow sampling and slow adaptation rate is simple and economic, thus a careful choice of sampling rate and adaptation rate is highly recommended. This thesis provides such guidelines for choosing proper sampling rate and adaptation rate for robust discrete adaptive control.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

The main exercise of this thesis is the formulation of a mathematical framework for analyzing an existing industrial adaptive control algorithm labeled Model weighting adaptive control (MWAC). The algorithm is then analyzed under this framework. The exercise is complemented by a set of algorithmic additions aimed at solving questions that so far had remained open (e.g. the treatment of undermodelling errors). Those solutions, on the other hand build on results derived from the analysis.
A key result for analyzing the algorithm is that when an external excitation is applied (in the form of a control task such as a setpoint change), the adaptive controller behaves, in a short time that follows the application of the excitation, as a linear equation whose parameters are completely known at design time. It follows that during this short period, the input signal provided to the estimation subsystem is at least partially known (except for disturbances) and that the estimation virtually takes place in open loop. Using this information and assuming boundedness of the disturbance signals, it is possible to bound the behaviour of the adaptive system at an early stage.
With the MWAC algorithm, the plant model is formed by making a weighted sum of a finite number of possible plant models. It is shown that, under adequate conditions and in a time corresponding to the apparent plant delay, the plant model will "jump" to a neighborhood of the true plant. The size of this neighborhood will depend in part on how sharply the bad models are discriminated from the good models. On the other hand, disturbances will smooth the weight map towards a uniform distribution. The sharpness or smoothness of the weight map can be measured online by computing the sum of the square root of all the weights in the set. The remarkable property of this measure is that an upper bound on the distance between the true plant and its model can be found which an affine function of the measure.
The effect of external disturbances such as measurement errors can be reduced by an external excitation of sufficient magnitude. This is not true however of disturbances caused by undermodelling errors which are almost always present to a lesser or greater degree. Two solutions are proposed to counteract this undesirable effect. The first method consists in bandpass filtering the input/output data in such a way that the frequency content of the data is consistent with data obtained from some first order plus delay (FOPD) model. The second method adjusts the sampling period online such that a compromise between satisfying the FOPD assumption and the coarseness of the control is obtained.

This doctoral thesis focuses on the modification of ACC to include actual driver workload in the context of automatic headway change. ACC is a driver assistance system that automatically maintains a preliminary defined speed and permits a driver to perform manual headway changes. As drivers show worse (brake) reaction times under high workload situations, the system increases headway accordingly. Driver workload is estimated based on physiological data. Here, we investigate the possibilities of such a system, assuming that physiological sensors can be implemented in future vehicles. The thesis consists of three parts: In the first part, the theoretical background is described and a suitable theoretical model is developed; in the second part, experiments are described, and in the last part, results are discussed. Altogether four experiments support this thesis: 1. The first experiment investigates the foundational relationships between physiology, brake reaction time and workload level. The study employs a simulator setting and results show that physiological data, such as heart rate and skin conductance, permit the identification of different workload levels. These findings validate the results of other studies showing that workload leads to an increase in reaction time. These results could only be validated between the extremes “no-workload” and “high workload” situations. 2. The second experiment simulates an ideal workload-adaptive cruise control (WACC) system. In a simulator setting, system acceptance and awareness are studied, with a view toward future implementation in a real car. The results show better acceptance of WACC in comparison with ACC when subjects receive additional information about the new system. This is because subjects do not perceive changes in distance under high workload conditions. 3. The third experiment focuses on acceptance of the simulated system in on-road conditions. In this study, WACC is integrated in the car and is operated using a MATLAB model. The experiment shows that more subjects notice changes in distance in the on-road condition. In general WACC is preferred over ACC; it is especially these subjects who do not notice changes in distance, who value WACC more than ACC. With the aim of implementing an operational WACC that is capable of adjusting distance according to changes in physiological data, a workload algorithm is developed. 4. The fourth experiment validates the workload algorithm. Results of the algorithm are compared with recordings of the activated workload task and detection rate is calculated. The detection of workload periods was feasible in nearly every case and detection rate was favorable, especially if one considers lags due to design-related latency periods. The experiments presented here indicate that workload is detectable in physiological data and that it influences brake reaction time. Further, we provide evidence pointing to the technical possibility of implementing WACC as well as positive acceptance. The results have been published as an article and are part of this thesis. Also, some parts of the thesis are published as a book chapter (see footnotes). Another publication is in preparation, coauthored by diploma thesis students, who are supervised by the author (consult footnotes). This dissertation is composed, in part, of these publications. References to page numbers of the diploma theses are given to ensure correspondence. The author escorted the topic WACC from the beginning to the end. Sometimes students were involved and intensively supervised, from a thematic as well as a personnel guidance perspective. The author planned the whole project and executed studies and calculations. His psychology insights were not only limited to the discipline of psychology but were furthermore, with the help of students, interdisciplinarily expanded to the subject of informatics. Every study and every result which is presented within this work, was conducted or achieved by the author or (if students supported him) was discussed with the author in weekly discussions (and often several times a day). In these discussions the author provided new ideas and corrections if necessary. Apart from that, the author looked after the fulfillment of the central theme, implemented his psychological knowledge on a daily basis and provided his expertise to complement interdisciplinary point of views. He discussed the central theme as well as details with external partners like the MIT AgeLab as well as professors of the European Union from the adaption project (a project aimed at educating future researchers which includes involvement of highly important commercial and educational partners) and beyond. In this time he also visited conferences and accumulated knowledge which led to the successful achievements of the main objective and he was relevant in reaching the common goals of the adaption project. Furthermore he presented the results of the scientific work on a conference, workshops and in written publications. Within BMW Group Research and Technology, he identified important department- and project- partners and combined the knowledge to a result which benefits science and economy
In dieser Dissertation wird eine Abwandlung des Active Cruise Control (ACC) untersucht, das zusätzlich die Belastung (Workload) des Fahrers als Parameter betrachtet, um den Abstand zum Vordermann automatisiert zu verändern. Bei diesem ACC handelt es sich um ein Fahrerassistenzsystem, das automatisiert die eingestellte Geschwindigkeit hält und eine manuelle (durch den Nutzer ausgelöste) Abstandsveränderung zum Vordermann ermöglicht. Da sich die Bremsreaktionszeit von Fahrern in hohen Belastungssituationen verschlechtert, soll das entwickelte Workload-adaptive Cruise Control (WACC) in Situationen hoher Belastung den Abstand zum Vordermann automatisiert erhöhen. Die Belastung des Fahrers soll durch physiologische Daten ermittelt werden. Die vorliegende Arbeit untersucht die Möglichkeit eines solchen Systems unter der Annahme, dass in Zukunft geeignete physiologische Sensoren ins Auto eingebaut werden können. Die Arbeit besteht aus drei Teilen: • Im ersten Teil wird der theoretische Hintergrund beschrieben und ein passendes theoretisches Modell entwickelt. • Im zweiten Teil werden die durchgeführten Experimente beschrieben. • Im dritten Teil werden die Ergebnisse diskutiert. Insgesamt wurden im Rahmen dieser Arbeit vier Experimente durchgeführt: Das erste Experiment beschäftigte sich mit den grundlegenden Zusammenhängen zwischen Physiologie, Bremsreaktionszeit und Belastungslevel. Wie die Ergebnisse der im Simulator durchgeführten Studie zeigen, können mit physiologischen Daten wie Herzrate, Herzratenvariabilität und Hautleitfähigkeit unterschiedliche Workloadlevel identifiziert werden. Darüber hinaus wurden die Ergebnisse anderer Studien bestätigt, die belegen, dass Workload die Bremsreaktionszeit erhöht, wobei dies nur im Kontrast zwischen den Extrembereichen „kein Workload“ und „hoher Workload“ nachweisbar ist. Das zweite Experiment diente der Simulierung eines perfekten WACC. Im Simulator wurden Akzeptanz und Systemwahrnehmung getestet, um vor der Implementierung in ein Realfahrzeug weitere Erkenntnisse zu gewinnen. Im Vergleich zum ACC wurde das WACC von den Probanden besser akzeptiert, nachdem sie zusätzliche Informationen zum neuen System erhalten hatten. Der wesentliche Grund dafür ist, dass die Probanden ohne Informationen die Abstandsveränderung bei hohem Workload nicht realisieren. Das dritte Experiment fokussierte auf die Akzeptanz des simulierten Systems unter Realbedingungen. Das WACC wurde in das Auto integriert und durch ein MATLAB Modell gesteuert. Als Ergebnis zeigte sich, dass unter Realbedingungen mehr Probanden die Abstandsveränderung realisieren als im Simulator. Generell wird das WACC präferiert – vor allem jene Probanden, die die Abstandsveränderung nicht realisieren, bewerten das WACC besser als das ACC. Mit den in diesem Teilexperiment erhobenen Daten wurde ein Algorithmus zur Workloaderkennung entwickelt. Auf dieser Basis konnte im letzten Experiment ein Realsystem implementiert werden, das aufgrund physiologischer Daten den Abstand verändert. Das vierte Experiment beschäftigte sich mit der Validierung des Algorithmus zur Workloaderkennung. Die Ergebnisse des Algorithmus wurden mit der aufgezeichneten Aktivierung des Workloadtasks verglichen und eine Detektionsrate ermittelt. Die Detektion der Workloadperioden gelingt in fast allen Fällen und die Detektionsrate ist vielversprechend, gerade wenn man Verzögerungen berücksichtigt, die wegen der Latenzzeit körperlicher Reaktionen nicht verbesserungsfähig sind. In den vorliegenden Experimenten konnte gezeigt werden, dass Workload über die Physiologie messbar ist und sich auf die Bremsreaktionszeit auswirkt. Darüber hinaus wurde gezeigt, dass ein WACC technisch machbar ist und die Ergebnisse lassen außerdem auf eine hohe Akzeptanz schließen. Die Forschungsergebnisse wurden in einem Artikel publiziert, der auch in dieser Dissertation zu finden ist. Teile der vorliegenden Arbeit wurden außerdem als Buchkapitel veröffentlicht (siehe Fußnoten), eine weitere Publikation mit den vom Autor umfassend betreuten Diplomanden ist in Ausarbeitung. Um die Nachvollziehbarkeit zu gewährleisten, wurde auf die Seitenzahlen der entsprechenden Diplomarbeiten verwiesen. Als Autor dieser Dissertation habe ich das Thema WACC von Anfang bis Ende selbst erarbeitet bzw. wurden Studenten eingesetzt und angeleitet, wo es sinnvoll erschien. Neben der fachlichen wie personellen Führung der Studenten umfassten meine Aufgaben die Planung des Gesamtprojekts, das Durchführen der Studien und die Berechnung von Kennzahlen – sowohl in meinem eigenen psychologischen Fachgebiet als auch interdisziplinär mit Hilfe von Experten aus der Informatik. Jedes Ergebnis, das in dieser Arbeit präsentiert wird, wurde entweder von mir selbst erzielt oder – sofern ich dabei von Studenten unterstützt wurde – mit mir in wöchentlichen (oft auch mehrmals täglich stattfindenden Meetings) besprochen. Wichtig war mir als Autor, einen durchgängigen Weg zur Entwicklung eines WACC zu wahren, mein psychologisches Fachwissen täglich anzuwenden und in interdisziplinären Aufgaben und Diskussionen meine Perspektive einzubringen. Besonders wichtig war dabei die Diskussion des Gesamtprojekts und der Details mit externen Partnern wie dem MIT AgeLab oder Professoren aus der Europäischen Union im Rahmen des Adaptation Projekts (ein von der EU gefördertes Projekt zur Ausbildung von Forschern, unter Einbindung wirtschaftlich und wissenschaftlich hochrangiger Partner). Durch die Präsentation der Ergebnisse auf Konferenzen, in Workshops und Publikationen konnte ich einen Beitrag dazu leisten, um die Adaptation-Ziele zu erreichen. Innerhalb der BMW Group Forschung und Technik habe ich darauf geachtet, relevante Schnittstellen- und Projektpartner zu identifizieren und das erlangte Wissen zu einem Ergebnis zu verbinden, das Wissenschaft und Wirtschaft gleichermaßen nützt

The evolution of Adaptive Shock Control Bumps (SCB) presented in this work is a result of an investigation into transonic flow control devices. The primary application is on transonic passenger jet aircraft during cruise and within supersonic intakes. It is in these locations that normal shocks of strength Mach 1.2-1.5 typically occur which SCB aim to manipulate and exploit. The original concept was developed by [Ashill et al., 1992] featured a flexible plate with actuators beneath the surface. The complex fluid-structure interaction (FSI) between the shock and plate has been a focus of this work with panel flutter studies heavily supplementing traditional SCB design methods. Adaptive SCB represent a required approach in order to negate poor off-design performance of static SCB. A coupled 2D aero-structural solver was developed using OpenFOAM [2017] and ABAQUS [2007] which provides a tool to evaluate the effects of varying plate proper- ties. Cavity pressure, plate length, thickness and material stiffness were found to be influential in the overall performance with four test cases developed. With lengths, lb = 150 − 200 mm and thicknesses t = 0.4 − 0.6 mm using Al-7075-T6, experimental models were produced and tested in a Mach 1.4 blowdown supersonic wind tunnel. These were sized using the coupled solver to deform passively and trigger the bifurca- tion of the shock. This new type of device showed potential for a passive adaptive SCB however all suffered from varying amounts of reacceleration over the rear surface. An aero-structural optimisation procedure is performed to position displacement con- straints beneath the flexible plate to control surface curvature. This was the driving force behind the detrimental reacceleration. The optimiser used a new performance metric which focussed upon smearing the adverse pressure gradient across the SCB which reduced the likelihood of plastic deformation. Optimal SCB have been shown to deploy and retract beneath an unsteady shock and successfully bifurcated the shock on demand.

This work presents a centralized control scheme applied to a power system. The scheme has adaptive characteristics which allow the controller to keep track of the changing power system operating point and to control nonlinear functions of state variables. Feedback to the controller is obtained from phasor measurements at chosen power system buses, generator field voltage measurements, and state estimators. Control effort is aimed at minimizing the oscillations and influencing the power system state trajectory through the control of linear and nonlinear functions of state variables during a power system disturbance. The main contributions of this dissertation are the simultaneous introduction and utilization of measurement based terms in the state and output equations in the derivation and implementation of the control law, the study of limits on controller performance as the state residual vector becomes very large, and the simulation of the performance of local state estimators to prove the need for faster phasor measurement systems. The test system is a hypothetical 39-Bus AC power system consisting of typical components which have been sufficiently modelled for the simulation of power system performance in a dynamic stability study.
Ph. D.

Human expert drivers have the unique ability to build complex perceptive models using correlated sensory inputs and outputs. In the case of longitudinal vehicle traction, this work will show a direct correlation in longitudinal acceleration to throttle input in a controlled laboratory environment. In fact, human experts have the ability to control a vehicle at or near the performance limits, with respect to vehicle traction, without direct knowledge of the vehicle states; speed, slip or tractive force. Traditional algorithms such as PID, full state feedback, and even sliding mode control have been very successful at handling low level tasks where the physics of the dynamic system are known and stationary. The ability to learn and adapt to changing environmental conditions, as well as develop perceptive models based on stimulus-response data, provides expert human drivers with significant advantages. When it comes to bandwidth, accuracy, and repeatability, automatic control systems have clear advantages over humans; however, most high performance control systems lack many of the unique abilities of a human expert. The underlying motivation for this work is that there are advantages to framing the traction control problem in a manner that more closely resembles how a human expert drives a vehicle. The fundamental idea is the belief that humans have a unique ability to adapt to uncertain environments that are both temporal and spatially varying. In this work, a novel approach to traction control is developed using an anthropomimetic control synthesis strategy. The proposed anthropomimetic traction control algorithm operates on the same correlated input signals that a human expert driver would in order to maximize traction. A gradient ascent approach is at the heart of the proposed anthropomimetic control algorithm, and a real-time implementation is described using linear operator techniques, even though the tire-ground interface is highly non-linear. Performance of the proposed anthropomimetic traction control algorithm is demonstrated using both a longitudinal traction case study and a combined mode traction case study, in which longitudinal and lateral accelerations are maximized simultaneously. The approach presented in this research should be considered as a first step in the development of a truly anthropomimetic solution, where an advanced control algorithm has been designed to be responsive to the same limited input signals that a human expert would rely on, with the objective of maximizing traction. This work establishes the foundation for a general framework for an anthropomimetic control algorithm that is capable of learning and adapting to an uncertain, time varying environment. The algorithms developed in this work are well suited for efficient real time control in ground vehicles in a variety of applications from a driver assist technology to fully autonomous applications.
Ph. D.

This dissertation addresses fundamental theoretical problems relevant to flight control for aerial vehicles and weapons in highly uncertain dynamical environment. The approach taken in this dissertation is the L1 adaptive control, which is elaborated from its design perspective for output feedback solution and is extended to time-varying reference systems to support augmentation of gain-scheduled baseline controllers. Compared to conventional adaptive controllers, L1 control has the following advantages: i) it has guaranteed uniformly bounded transient response for system's both signals, input and output; ii) it enables fast adaptation while maintains a bounded away from zero time-delay margin. The proposed adaptive control approach can recover the nominal performance of the flight control systems in the presence of rapid variation of uncertainties. Furthermore, the benefit of L1 adaptive control is its promise for development of theoretically justified tools for Verification and Validation (V&V) of adaptive systems. Adaptive control for uncertain systems usually needs to handle two types of uncertainties: matched and unmatched uncertainties. Both of these two uncertainties will appear in practical flight control problems. In this dissertation, adaptive approaches which can compensate for these two types of uncertainties will be discussed respectively. Two architectures of L1 adaptive control, namely L1 state feedback adaptive control and L1 output feedback adaptive control, are studied. The state feedback adaptive control is applied for compensation of matched uncertainties. Although the state feedback scheme is capable of handling certain type of unmatched uncertainties, such approach is not explored in this dissertation. On the other hand, the output feedback approach is mainly aimed to solve problems in the presence of unmatched uncertainties. The dissertation first discusses the state feedback L1 adaptive control for time-invariant reference systems. The adaptive controller is designed to augment an existing baseline controller. The closed loop system of the plant and the baseline controller is time-invariant. This closed loop system, which is a Linear Time Invariant (LTI) system, determines the dynamics of the reference system. The adaptive feedback can compensate for nonlinear state- and time-dependent uncertainty with uniformly bounded transient response. In this dissertation we discuss the Multi-Input Multi-Output (MIMO) extension of the method. Two flight control examples,Unmanned Combat Aerial Vehicle (UCAV) and Aerial Refueling Autopilot, are considered in the presence of nonlinear uncertainties and control surface failures. The L1 adaptive controller without any redesign leads to scaled response for system's both signals, input and output, dependent upon changes in the initial conditions, system parameters and uncertainties. The time-delay margin analysis for these two examples verifies the theoretical claims. Next, the output feedback approach is studied. The adaptive output feedback controller can be applied to reference systems that do not verify the Strict Positive Real (SPR) condition for their input-output transfer function. In this dissertation, specific design guidelines are presented that render the approach suitable for practical applications. A missile autopilot design example is given to demonstrate the benefits of the design approach. Finally, the L1 state feedback adaptive controller is extended to time-varying reference systems. The adaptive controller intends to augment a gain-scheduled baseline controller. The reference system, which is determined by the closed loop system of the plant and the baseline gain-scheduled controller, is time-varying. The adaptive controller with time-varying reference system is proved to have guaranteed performance bounds similar to those obtained for the case of linear time-invariant reference systems. With this result, the aerial refueling application can be extended to a complete scenario, which includes a racetrack maneuver for an aircraft. The concluding chapter discusses the challenging issues for future research.
Ph. D.

Major problems of the Foot Drop treatment are expensive and complex solutions. This work

presents the performance of a new inexpensive method named as Semi-Active Ankle Foot

Orthosis (SAAFO). The concept of this approach is to use inexpensive sensors to detect foot step

movement. The signals from the sensors afterwards will be fed to a control system of SAAFO in

runtime for a smooth foot movement of a drop foot patient while walking. Different sensors have

been studied in detail along with comparison to the proposed sensor system and mechanical

design. The signals from the sensors are used to detect different phases of human walking. These

sensors are placed at different positions on an orthosis and their signals are studied in detail.

Experiments have been done in different conditions to get a realistic picture either this assembly

can be implemented commercially. Signals are plotted and discussed yielding that the human

walking phases can be easily and accurately detected using inexpensive sensor assembly.

iv ABSTRACT ADAPTIVE CONTROL OF GUIDED MISSILES Tiryaki Kutluay, Kadriye Ph.D., Department of Aerospace Engineering Supervisor: Asst. Prof. Dr. Ilkay Yavrucuk February 2011, 147 Pages This thesis presents applications and an analysis of various adaptive control augmentation schemes to various baseline flight control systems of an air to ground guided missile. The missile model used in this research has aerodynamic control surfaces on its tail section. The missile is desired to make skid to turn maneuvers by following acceleration commands in the pitch and yaw axis, and by keeping zero roll attitude. First, a linear quadratic regulator baseline autopilot is designed for the control of the missile acceleration in pitch axis at a single point in the flight envelope. This baseline autopilot is then augmented with a Direct Model Reference Adaptive Control (DMRAC) scheme using Neural Networks for parameter estimation, and an L1 Adaptive Control scheme. Using the linearized longitudinal model of the missile at the design point, simulations are performed to analyze and demonstrate the performance of the two adaptive augmentation schemes. By injecting uncertainties to the plant model, the effects of adaptive augmentations on the linear baseline autopilot are examined. v Secondly, a high fidelity simulation software of the missile is used in order to analyze the performance of the adaptive augmentations in 6 DoF nonlinear flight simulations. For the control of the missile in three axis, baseline autopilots are designed using dynamic inversion at a single point in the flight envelope. A linearizing transformation is employed during the inversion process. These coarsely designed baseline autopilots are augmented with L1 adaptive control elements. The performance of the adaptive control augmentation system is tested in the presence of perturbations in the aerodynamic model and increase in input gain, and the simulation results are presented.

This thesis documents the development of an adaptive controller designed to control the elastic aircraft dynamics of a generic general aviation aircraft. The elastic aircraft equations of motion are derived using Lagrange’s equation and the principal of virtual work. A minimum kinetic energy axis system is chosen as the body reference axis, which results in structural equations of motion that are decoupled from the rigid body equations of motion. An aerodynamic strip method is utilized to develop closed-form expressions for the longitudinal generalized structural forces. The adaptive controller is designed using a model reference adaptive control scheme, modified for general aviation to use an “E-Z fly” decoupled control architecture, which tracks vertical flight path angle and true airspeed. The adaptive control signal is computed using a weighted least mean square optimization, which gives the control designer more influence on the behavior of the adaptation. A notch filter is designed to decouple the controller and adaptation from the structural modes. The controller is implemented in the MATLAB®/Simulink® environment, and the equations of motion are integrated in simulation for a range of structural flexibility and plant failures. Results show that the controller is capable of handling the uncertainties associated with unmodeled aeroelastic modes. Additionally, the controller shows resilience to “A” and “B” matrix failures, such as 25% loss in elevator and throttle effectiveness. Actuator speed is found to limit the amount of failure the system can recover from, where a fast actuator facilitates adaptation to much larger failures. The notch filter is shown to be successful at decoupling the controller from the structural modes, even for a highly flexible aircraft. Performance without the notch filter is not degraded when the structural modes are outside the controller bandwidth; however, when structural modes fall within the controller bandwidth, the notch filter is required to damp excessive control activity. The proposed controller shows balance between good tracking performance and time delay margin, which is a measure of robustness in the system. This is attributed to the weighted least mean square optimization procedure that gives the control designer more influence over the behavior of the adaption.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Aerospace Engineering.

The Navy is interested in developing systems for horizontal, near ocean surface, high-energy laser propagation through the atmosphere. Laser propagation in the maritime environment requires adaptive optics control of aberrations caused by atmospheric distortion. In this research, a multichannel transverse adaptive filter is formulated in Matlab's Simulink environment and compared to a complex lattice filter that has previously been implemented in large system simulations. The adaptive filters are used to augment a classical adaptive optics controller and are also compared to a Kalman filter augmenting a classical controller. Additionally, the Naval Postgraduate School's first laboratory testbed to use adaptive optics for the compensation of atmospheric turbulence is designed and built. The control algorithms are evaluated both in simulation and in the presence of a laboratory-generated disturbance. Finally, effects of horizontal propagation through deep turbulence are created in the lab. Beam control algorithms are tested in this environment to draw initial conclusions about performance in deep turbulence. For the system implemented in this research, the simple transverse filter in combination with a classical proportional-integral controller performs comparably to the complex lattice filter and the Kalman filter in a standard turbulence scenario and demonstrates more robust performance in the deep turbulence scenario. The adaptive optics testbed itself can be transitioned easily between traditional and deep turbulence scenarios and can support a wide range of atmospheric realizations for further beam control research.

This thesis reports the successful application of the recently introduced Generalised Predictive Control self-tuner to the high-performance positioning of a real flexible single-link robot arm. The large amount of experimental time available on this high bandwidth system allowed exhaustive testing of the 'tuning-knobs' and 'design-filters' available to the user for tailoring the closed-loop. Based upon these experiments a coherent philosophy for configuring GPC in practice is generated. Configuration details found to be necessary for satisfactory GPC control of this high-order neutrally stable and non-minimum-phase plant, with its lightly damped resonant modes, are isolated. In particular it is found that band-pass filtering of data is essential for stable offset-free control using finite-order models of the plant. These aspects are considered in detail both theoretically and experimentally. In this application, as is often the case in practice, some information about the plant dynamics is available beforehand. Novel methods for the inclusion of this prior knowledge are introduced and their beneficial effects on the convergence of the recursive least squares estimation scheme, upon which most self-tuners are based, are demonstrated in simulation and experiment. A novel high-speed 68010/20 multi-processor computer system is described which allows the implementation of GPC at the required high sample rate (60Hz). The software division of the self-tuning algorithm into concurrently and sequentially executing tasks is discussed in detail.

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
É importante investigar formas de aumentar a eficiência de gráficos de controle estatístico de processos por atributos, porque o controle por atributos tipicamente exige tamanhos de amostra muito grandes para se obter um tempo rápido de sinalização de desvios no processo, e estes tamanhos de amostra podem ser altamente indesejáveis ou mesmo inviáveis em diversas situações práticas. Os esquemas adaptativos, que consistem em variar um ou mais dos parâmetros do gráfico de controle (tamanho de amostra, intervalo de tempo entre amostras e abertura dos limites de controle) de acordo com a informação precedente fornecida pelo gráfico (isto é, de acordo com a posição do último ponto registrado), proporcionam, para a detecção de desvios pequenos a moderados no processo, uma melhor eficiência que os esquemas tradicionais, de parâmetros fixos. Enquanto que esquemas adaptativos têm sido propostos para gráficos de controle por variáveis, para gráficos por atributos - apesar da necessidade e oportunidade acima mencionada de investigar formas de aumentar a eficiência de gráficos de controle estatístico de processos por atributos - há uma lacuna na literatura, a ser preenchida. Esta é a motivação do presente trabalho, que propõe um esquema adaptativo para gráficos decontrole por atributos, aplicável tanto a gráficos de np como de c, variando todos os parâmetros do gráfico. Desenvolve-se o modelo matemático para cálculo das medidas de desempenho; este modelo foi implementado em uma planilha eletrônica, o que permitiu analisar quantitativamente o desempenho do esquema em uma larga gama de casos, comparando-o ainda com o desempenho de gráficos tradicionais (com parâmetros fixos), e com o de outros esquemas adaptativos, com menor número de parâmetros variando. O esquema proposto mostrou-se sensivelmente mais eficiente na maioria das situações de interesse, respondendo portanto à necessidade, que motivou o trabalho, de buscar formas de aumentar a eficiência de gráficos por atributos. Resultados adicionais do trabalho são: a identificação das situações em que cada esquema é mais eficiente (ou do esquema mais eficiente em cada situação) e conjuntos de valores recomendados para os parâmetros dos gráficos em cada situação. Estes resultados facilitam a operacionalização da ferramenta para uso na prática.
It is important to investigate ways to improve the efficiency of the statistical control charts for attributes, because the controls for attributes typically require very large sample sizes for quick detection of changes in the process, and these sample sizes can be extremely undesirable or eventually unfeasible in many practical situations. The adaptive schemes, which vary one or more parameters of the control charts (sample size, sampling interval and control limit width) according to the most recent information about the process (the position of the last sample point in the chart), were proven to be more efficient than the traditional (fixed parameters) scheme in detecting small to moderate changes in the process. Although there is a large volume of work on adaptive schemes applied to variables control charts, there is a lack of adaptive schemes for control charts for attributes in the literature, despite the already mentioned need and opportunity of investigating ways to improve the efficiency of the statistical control charts for attributes. This is the motivation of this work. A fully adaptive scheme is proposed for both np and charts. The mathematical model is developed for the measures of performance; this model was implemented in a spreadsheet, and used for the quantitative evaluation of the scheme efficiency, and comparison with the fixed-parameter (traditional) scheme and also with other adaptive schemes, with only one or two parameters variable. The proposed scheme proved to be significantly more efficient in the majority of the situations of interest, answering the necessity of finding ways to improve the efficiency of control charts for attributes. Additional results of this work are: the identification of the situations in which each of the schemes here analyzed is the most efficient (or, the other way around: indication of the most efficient scheme for each situation), and a set of recommended values for the design parameters for each scheme, in each situation. These results are meant to make easier the adoption of the scheme in practice, increasing thereby its usefulness.
El desarrollo de técnicas que aumenten la eficiencia de gráficos de control estadístico de procesos por atributos tiene gran importancia porque el control por atributos típicamente exige tamaños de muestra muy grandes para obtener un tiempo rápido de señalización de desvíos en el proceso. En diversas situaciones prácticas estos tamaños de muestra pueden ser altamente indeseables o incluso inviables. Los esquemas adaptativos, que consisten en variar uno o más parámetros del gráfico de control (tamaño de muestra, intervalo de tiempo entre muestras y abertura de los límites de control) de acuerdo con la información precedente ofrecida por el gráfico (esto es, de acuerdo con la posición del último punto registrado), proporcionan, para la detección de desvíos pequeños a moderados, una mayor eficiencia que los esquemas tradicionales, de parámetros fijos. Mientras que los esquemas adaptativos han sido propuestos para gráficos de control por variables, para gráficos por atributos - a pesar de la necesidad y oportunidad mencionada de investigar formas de aumentar la eficiencia de gráficos de control estadístico de procesos por atributos - no existen muchas referencias en la literatura. Esta es la motivación del presente trabajo, que propone un esquema adaptativo para gráficos de control por atributos, aplicable tanto a gráficos de np como de c, variando todos los parámetros del gráfico. Se desarrolla el modelo matemático para el cálculo de las medidas de desempeño; este modelo fue implementado en una planilla electrónica, que permitió analizar cuantitativamente el desempeño del esquema en una amplia gama de casos, comparando con el desempeño de gráficos tradicionales (con parámetros fijos), y con el de otros esquemas adaptativos, con menor número de parámetros variando. El esquema propuesto se mostró sensiblemente más eficiente en la mayoría de las situaciones de interés que motivaron el trabajo. Resultados adicionales del trabajo son: la identificación de las situaciones en que cada esquema es más eficiente (o del esquema más eficiente en cada situación) y conjuntos de valores recomendados para los parámetros de los gráficos en cada situación. Estos resultados facilitan la operacionalización de la ferramenta para uso en la práctica.

Includes bibliographical references (p. 159-162).
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
(cont.) In the second setting, we propose a novel total field collision avoidance algorithm of magnetic nature which permits a set of vehicles to reconfigure successively without knowing each other's positions; strategic sensor positioning makes sure that the vehicles do not sense their own fields. Contributions of our research are a multiagent learning algorithm, a unified game theoretic framework for addressing reconfiguration problems, the identification of reconfiguration control as a problem common to several different fields but previously addressed with field-specific methods, the proposal of a definition of robustness in this context and, for the two trajectory planning settings in which our algorithm was implemented, two algorithms for distributed coordination and collision avoidance, respectively.
Distributed control systems are emerging as more robust and flexible alternatives to traditional control systems in several mechatronic fields such as satellite control and robotics. Instead of relying on one large unit with a centralized control architecture, one thus uses a parallel structure composed of many simple controllers collectively capable of performing the same task as the large unit. Reconfiguration control involves cooperation, coordination and mutual adaptation and is relevant in a number of engineering problems such as formation control, multiagent learning and role allocation. In addition to being a key issue for using the distributed control paradigm to its full potential, reconfiguration control also offers a well delimited framework for addressing a number of interesting theoretical questions in distributed control such as those related to the overlapping notions of cooperation and coordination. We propose a unified game theoretic approach to the problem of reconfiguration control which interprets node positions as strategies, identifies each configuration with the unique equilibrium of a game and sees reconfigurations as switches of games. Our approach is implemented in two different settings, both related to trajectory planning, and illustrated with simulation results. In the first setting, we propose replicator learning as a multiagent learning algorithm which is a generalization of the replicator dynamics and show convergence in any finite dimension I of the average strategy to any desired strategy as a function of the chosen game matrix. We show how this result can be linked to collective motion in a subspace of Rl-1 resulting in a successive visiting of a set of waypoints.
by Karin Sigurd.
Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.
Includes bibliographical references (p. 105-109).
The guidance, navigation and control of hypersonic vehicles are highly challenging tasks due to the fact that the dynamics of the airframe, propulsion system and structure are integrated and highly interactive. Such a coupling makes it difficult to model various components with a requisite degree of accuracy. This in turn makes various control tasks including altitude and velocity command tracking in the cruise phase of the flight extremely difficult. This work proposes an adaptive controller for a hypersonic cruise vehicle subject to: aerodynamic uncertainties, center-of-gravity movements, actuator saturation, failures, and time-delays. The adaptive control architecture is based on a linearized model of the underlying rigid body dynamics and explicitly accommodates for all uncertainties. Within the control structure is a baseline Proportional Integral Filter commonly used in optimal control designs. The control design is validated using a highfidelity HSV model that incorporates various effects including coupling between structural modes and aerodynamics, and thrust pitch coupling. Analysis of the Adaptive Robust Controller for Hypersonic Vehicles (ARCH) is carried out using a control verification methodology. This methodology illustrates the resilience of the controller to the uncertainties mentioned above for a set of closed-loop requirements that prevent excessive structural loading, poor tracking performance, and engine stalls. This analysis enables the quantification of the improvements that result from using and adaptive controller for a typical maneuver in the V-h space under cruise conditions.
by Travis Eli Gibson.
S.M.

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (p. 72).
A linear predictor and adaptive control loop are added to a conventional comparator to greatly reduce the delay. A linear predictor feeds an estimated future signal to the comparator to compensate for the comparator's internal delay. On a cycle-by-cycle basis, an adaptive controller adjusts the comparator's bias current to null the error. Emphasis is placed on low power consumption, including the development of a linear predictor with no static power consumption. Improvements of two orders of magnitude in power-delay product are demonstrated. The adaptive comparator is ideally suited for applications such as synchronous rectification but will also find broad applicability anywhere an asynchronous comparator is required, such as sensor interfaces, oscilloscope triggers, and some types of analog-digital converters.
by Alex C.H. MeVay.
M.Eng.

The drive for autonomy in manufacturing is making increasing demands on control systems, both for improved performance and for extra flexibility. This is reflected in the research and development of autonomously guided vehicles which must operate safely in ill-defined, complex and time-varying environments. Traditional control systems generally make infeasible assumptions which limit their application within this domain, and therefore current research has concentrated on Intelligent Control techniques in order to make the control systems flexible and robust. An integral part of intelligence is the ability to learn from a systems interaction with its environment, and this thesis provides a unified description of several adaptive neural and fuzzy networks. The recent resurgence of interest in these two anthropomorphic techniques has seen these algorithms widely applied within learning control systems, although a firm theoretical framework which can compare different networks and establish convergence and stability conditions has not evolved. Such results are essential if these adaptive algorithms are to be used in real-world applications where safety and correctness are prime concerns. The work described in this thesis addresses these questions by introducing a class of systems called associative memory networks, which is used to describe the similarities and differences which exist between certain fuzzy and neural algorithms. All of the networks can be implemented within a 3-layer structure, where the output is linearly dependent on a set of adjustable parameters. This allows parameter convergence to be established when a gradient descent training rule is used, and the rate of convergence can be directly related to the condition of the network's basis functions. The size, shape and position of these basis functions gives each network its own specific modelling attributes, since the learning rules are identical. Therefore it is important to study the network's internal representation as this provides information about how each network generalises (both interpolation and extrapolation), the rate of parameter convergence and the type of nonlinear functions which can be successfully modelled. Three networks are described in detail: the Albus CMAC, the is given of the Albus CMAC which illustrates its desirable features for on-line, nonlinear adaptive modelling and control: local learning and a computational cost which depends linearly on the input space dimension. The modelling capabilities of the algorithm are rigorously analysed and it is shown that they are strongly dependent on the generalisation parameter, and a set of consistency equations is derived which specify how the network generalises. The adaptive B-spline network, which embodies a piecewise polynomial representation, is also described and used for nonlinear modelling and constructing a static rule base which guides and autonomous vehicle into a parking slot. B-splines are also used for on-line, constrained trajectory generation where they approximate a set of velocity or positional subgoals. Fuzzy systems are typically ill-defined, although the approach taken in this thesis is to use algebraic rather than truncation operators and smooth fuzzy sets which means that the modelling capabilities of the fuzzy network can be determined exactly, and convergence and stability results can be derived for these algorithms. These results focus research on the learning, modelling and representational abilities of the networks by providing a common framework for their analysis. The desirable features of the networks (local learning, linearly dependent on the parameter set, fuzzy interpretation) are emphasised, and the algorithms are all evaluated on a common time series prediciton problem.

This study investigates an adaptive control scheme designed to maintain accurate motor speed control in spite of high-frequency periodic variations in load torque, load inertia, and motor parameters. The controller adapts, stores and replays a schedule of torques to be delivered at discrete points throughout the periodic load cycle. The controller also adapts to non-periodic changes in load conditions which occur over several load cycles and contains inherent integrator control action to drive speed error to zero. Using computer simulations, the control method was successfully applied to a 3Φ synchronous motor and a permanent magnet D.C. motor. The D.C. motor (or A.C. servo-motor) controller has superior characteristics and this system performance was compared to P, PI and PID control for two severe load cases - a periodic step load and a four-bar linkage load. Simulation studies showed the schedule control method to be stable and in comparison to the PID controller to have 1) nearly the same speed of response but without the overshoot found in PID control, 2) nearly the same mean speed error (~ O), 3) 12-50 times better reduction in speed fluctuation, and 4) the schedule controller gains were much easier to find than PID gains for this low-order, highly responsive system.
Master of Science

The author proposes a novel functional regression method for parameter estimation and adaptive control in this dissertation. In the functional regression method, the regressors and a signal which contains the information of the unknown parameters are either determined from raw measurements or calculated as the functions of the measurements. The novel feature of the method is that the algorithm maps the regressors to the functionals which are represented in terms of customized test functions. The functionals are updated continuously by the evolution laws, and only an infinite number of variables are needed to compute the functionals. These functionals are organized as the entries of a matrix, and the parameter estimates are obtained using either the generalized inverse method or the transpose method. It is shown that the schemes of some conventional adaptive methods are recaptured if certain test function designs are employed. It is proved that the functional regression method guarantees asymptotic convergence of the parameter estimation error to the origin, if the system is persistently excited. More importantly, in contrast to the conventional schemes, the parameter estimation error may be expected to converge to the origin even when the system is not persistently excited. The novel adaptive method are also applied to the Model Reference Adaptive Controller (MRAC) and adaptive observer. It is shown that the functional regression method ensures asymptotic stability of the closed loop systems. Additionally, the studies indicate that the transient performance of the closed loop systems is improved compared to that of the schemes using the conventional adaptive methods. Besides, it is possible to analyze the transient responses a priori of the closed loop systems with the functional regression method. The simulations verify the theoretical analyses and exhibit the improved transient and steady state performances of the closed loop systems.
Ph. D.

Unmanned Aerial Vehicles (UAV) control has become a very important point of scientific study. The control design challenges of a UAV make it one of the most researched areas in modern control applications. This thesis specifically chooses the Quadrotor as the UAV platform. Considering the quadrotor has 4 rotors and 6 degrees of freedom, it is an underactuated system and is dynamically unstable that has to be stabilized by a suitable control algorithm in order to operate autonomously. This thesis focuses on the quaternion representation of the quadrotor system dynamics and develops an adaptive control for its trajectory tracking problem. The control design uses the certainty equivalence principle where adaptive tracking controls are designed separately for each of the translational and rotational subsystems. With this approach, the success of the outer loop translational control relies on the fast convergence of the inner loop rotational control in order to guarantee the system’s stability while achieving the tracking objective. For the translational subsystem in the outer loop, a modified geometric control technique is considered with an adaptive component for the estimation of the uncertain mass of the quadrotor. For the rotational subsystem in the inner loop a backstepping based control design is adopted due to its systematic design and intuitive approach. An adaptive component is further integrated with it to estimate the integrated components of the uncertain Moment of Inertia matrix and other constant parameters in the system dynamics to guarantee the stability of the inner loop system while achieving the tracking objective. Furthermore, a complete backstepping control design methodology is presented which overcomes the issues of certainty equivalence principle where the inner loop needs to execute significantly faster than the outer loop to stabilize the system.

Many classical control methods are based upon assumptions of linearity and stationarity of the process to be controlled. For the case of motion control of a land vehicle in an unstructured outdoor environment these assumptions do not hold, due to complex vehicle interactions with its surroundings and time--varying environmental conditions. The large number of possible future platforms leads to the desire to produce motion controllers which are generally applicable to a wide range of vehicles with little a priori knowledge of vehicle dynamics. Intelligent, self--learning, systems promise many of the desired features for such controllers. This thesis investigates the use of intelligent controllers for autonomous land vehicle motion control. A new class of fuzzy controller, the indirect adaptive fuzzy controller is proposed as a possible solution to this problem. This controller is then developed by combining on--line adaptive modelling with model causality inversion and on--line controller design. The resulting controller is an analogue of the indirect adaptive algebraic controller. A major advantages of this method is the separation of model convergence and control loops enabling the two aspects to be analysed separately. Demonstration of this work has been achieved by a series of simulation tests using a variety of vehicle models. A conventional front wheel steer road vehicle model has been used as well as two IFAC benchmark control problems (ship autopilot and passenger bus) to investigate the properties of the controller. To test the controller with realistic demand signals, a static rule-based piloting system has also been developed. These simulations have demonstrated i) the successful control of systems with little a priori vehicle knowledge ii) ability to adapt to continuous and sudden parametric changes in the process iii) good noise rejection properties iv) good disturbance rejection properties and v) ability to adapt to stationary loop non--linearities.

This work is a study of a hybrid adaptive controller that blends fixed feedback control and adaptive feedback control techniques. This type of adaptive controller removes the requirement that information about the disturbance is known apriori. Additionally, the control structure is implemented in such a way that as long as the adaptive controller is stable during adaptation, the system consisting of the controller and plant remain stable. The objective is to design and implement an adaptive controller that damps the structural vibrations induced in a multi-modal structure. The adaptive controller utilizes an adaptive infinite impulse response lattice filter for improved damping over the fixed feedback controller alone. An adaptive finite impulse response LMS filter is also implemented for comparison of the ability for both algorithms to reject harmonic, narrow bandwidth and wide bandwidth disturbances. It is demonstrated that the lattice filter algorithm performs slightly better than the LMS filter algorithm in all three disturbance cases. The lattice filter also requires less than half the order of the LMS filter to get the same performance.
Master of Science