Accurate models are necessary in critical applications. Key parameters in dynamic systems often change during their life cycle due to repair and replacement of parts or environmental changes. This dissertation presents a new approach to update system models, accounting for these changes. The approach uses frequency domain data and a neural network to produce estimates of the parameters being updated, yielding a model representative of the measured data. Current iterative methods developed to solve the model updating problem rely on minimization techniques to find the set of model parameters that yield the best match between experimental and analytical responses. Since the minimization procedure requires a fair amount of computation time, it makes the existing techniques infeasible for use as part of an adaptive control scheme correcting the model parameters as the system changes. They also require either mode shape expansion or model reduction before they can be applied, introducing errors in the procedure. Furthermore, none of the existing techniques has been applied to nonlinear systems. The neural network estimates the parameters being updated quickly and accurately without the need to measure all degrees of freedom of the system. This avoids the use of mode shape expansion or model reduction techniques, and allows for its implementation as part of an adaptive control scheme. The proposed technique is also capable of updating weakly nonlinear systems. Numerical simulations and experimental results show that the proposed method has good accuracy and generalization properties, and it is therefore, a suitable alternative for the solution of the model updating problem of this class of systems. / Ph. D.

Robotic arms are essential in a variety of industrial processes. However, the dexterous workspace of a robotic arm is limited. This limitation can be overcome by making the robotic arm mobile. Such robots, which comprise a robotic manipulator installed on a wheeled mobile platform, are called mobile robots. A mobile manipulator can attain a position in space which a robotic arm with fixed base may not be able to reach otherwise. To be applicable to a variety of scenarios, these robots need to meet user-defined margins on their trajectory tracking error, irrespective of the payload transported, faults, and failures. In this thesis, we study the dynamics of mobile manipulator comprising both a differential-drive mobile robot (DDMR) and a robotic arm. Thus, we design a model reference adaptive controller (MRAC) for this mobile manipulator to regulate this vehicle and guarantee robustness to uncertainties in the robot's inertial properties such as the mass of the payload transported and friction coefficients. / Master of Science / Humans are able to perform tasks effectively owing to their extraordinary sense of perception and due to their ability to easily grasp things. Although humans are well-suited to perform any process, within an industrial context, a variety of tasks might pose danger to humans, like dealing with hazardous materials or working in extreme environments. Moreover, humans may suffer from fatigue while performing repetitive tasks. These considerations gave rise to the idea of robots which could do the work for humans and instead of humans. Mobile manipulators are a kind of robot that is well-suited for performing a variety of tasks such as collecting, manipulating, and deploying objects from multiple locations. In order to make robots perform a user-specified task, we need to study how the robot reacts to external forces. This knowledge helps us derive a mathematical model for the robotic system. This dynamical model would then be essential in controlling the motion of the robot. In this thesis, we study the dynamics of a mobile manipulator, which comprises a two-wheeled ground platform and a five degrees-of-freedom robotic arm. The dynamical model of this mobile robot is then employed to design a controller that guarantees user-defined margins of error despite uncertainties in some properties, such as the mass of the payload transported.

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.

This thesis discusses the use of genetic algorithms as a global search technique to solve three optimization problems: a sixth-order polynomial problem, a single-degree-of-freedom spring-mass-damper (SDOF SMD) system problem, and a loading bridge regulator problem. Genetic algorithms are iterative global search techniques based on the principles of natural selection and population genetics. The theory, design and implementation of the algorithm is discussed in detail. The Simple Genetic Algorithm (SGA) is presented to solve a sixth-order polynomial optimization problem. Results from two traditional numerical techniques will be compared with the SGA results as well as the analytical calculus solution. In addition, the effect of different parametric sizes of the genetic operators are investigated. In the second problem, genetic algorithms are used to design a two-state feedback optimal gain set for a SDOF SMD model with a given initial condition. An improved selection scheme called the stochastic remainder selection without replacement is introduced. An improved GA-based (IGA) feedback controller is designed to control the system. Lastly, a regulator control problem is presented using advanced genetic algorithms (AGA). Two-point crossover and inversion operators are employed. A loading bridge is chosen as the control model. An advanced GA-based full-state feedback controller is designed to control the loading bridge with the given reference input voltage. The conclusions show that SGA is more robust than traditional numerical techniques to solve multi-modal functions. Among the three GA approaches considered, AGA is the most robust one for the design of adaptive feedback controllers. / Master of Science

Three discrete-time adaptive controllers are developed and applied to Four-bar linkage velocity control to reduce the input link velocity fluctuations without compromising the control system velocity transient response. The successful control techniques use the known mechanism kinematics and the mechanism input link position to control the nonlinear mechanism dynamics. The study shows that the adaptive controls are feasible to implement using current microprocessor technology, and the velocity control performance is improved when compared to an industry-standard analog servomotor control. However, more development is required to realize the full potential of the adaptive control technique. A nonlinear Four-bar dynamic model is developed using Kinematic Influence Coefficients. This model is used to develop the adaptive controls and to computer simulate the control scheme performances. The simulated model velocity response is compared qualitatively to experimental data and shown to be similar to an experimental device. / Master of Science

Techniques are available today to attenuate the output sound of the trumpet. All of these techniques involve using passive mutes. Due to the limitations in the sound one can obtain with passive mutes, another solution, using active noise control, is proposed to predictably attenuate the output sound of the trumpet. With the new system, it is theorized any desired output sound can be obtained. Within this thesis a model of the trumpet physics is derived and an investigation of the implementation of two analog feedback controllers and two digital LMS controllers is performed. The model of the trumpet mechanics is studied to understand the trumpet system before applying the control systems. Analysis is performed on the type and the location of the acoustic control actuator and the error sensor to be used. With the chosen actuator and sensor, the two types of controllers are designed and realized. The farfield spectrum of the trumpet's response to a single note is analyzed for each controller and the resulting attenuations compared. The model of the trumpet system is then used to demonstrate the coupling of the trumpet and the player and to show the effects of the controllers on the behavior of the player's embouchure. With the inclusion of the controllers in the trumpet system, the farfield spectrum was successfully attenuated at two harmonics of the tone passed through the trumpet. Testing was not performed with an actual trumpet player due to the high sound pressure levels (160 dB SPL) required from the control actuator. From a derived model of the control actuator, specifications for an acoustic driver capable of delivering the high sound pressure level were calculated. Design and fabrication of the proposed actuator will be completed during future work. / Master of Science

This thesis shows the implementation of a novel control scheme DC-DC converter. The converter is a phase-shifted full-bridge PWM converter that is designed to operate as a front stage of a power conversion system where the input is a variable low voltage high current source. The converter is designed to step-up the low voltage input to an acceptable level that can be inverted to a 120/240 VAC 60Hz voltage for residential power. A DSP based adaptive control model is developed, taking into account line variations introduced by the input source while providing very good load dynamics for the converter in both discontinuous and continuous conduction modes. The adaptive controller is implemented using two voltage sensors that read the input and the output voltages of the converter. The controller's bandwidth is comparable to current mode control, without the need for an expensive current sensor, yet providing the noise immunity seen in voltage mode controllers. The intended input source was a fuel cell but in its absence a DC supply is utilized instead. The system is simulated for both discontinuous and continuous conduction modes and implemented and demonstrated for the continuous conduction mode. The test results are shown to match the simulation results very closely. / Master of Science

Tailsitter UAVs (Unmanned Aerial Vehicles) are a type of VTOL (Vertical Take off and Landing) aircraft that combines the agility of a quadrotor drone with the endurance and speed of a fixed-wing aircraft. For this reason, they have become popular in a wide range of applications from tactical surveillance to parcel delivery. This thesis details a clean sheet design process for a tailsitter UAV that includes the dynamic modeling, control design, simulation, vehicle design, vehicle prototype fabrication, and testing of a tailsitter UAV. The goal of this process was to design a robust controller that is able to handle uncertainties in the system's parameters and external disturbances and subsequently can control the vehicle through the transition between vertical and horizontal flight regimes. It is evident in the literature that most researchers choose to model and control tailsitter UAVs using separate methods for the vertical and horizontal flight regimes and combine them into one control architecture. The novelty of this thesis is the use of a single dynamical model for all flight regimes and the robust control technique used. The control algorithm used for this vehicle is a MRAC (Model Reference Adaptive Control) law, which relies on reference models and gains that adapt according to the vehicle's response in all flight regimes. To validate this controller, numerical simulations in Matlab and flight tests were conducted. The combination of these validation methods confirms our adaptive controller's ability to control the transition between the vertical and horizontal flight regimes when faced with both parametric uncertainties and external disturbances. / Master of Science / Unmanned aircrafts have been a topic of constant research and development recently due to their wide range of applications and their ability to fly without directly involving pilots. More specifically, VTOL UAVs have the advantage of being able to take off without a runway while retaining the efficiency of a classical aircraft. A tailsitter UAV behaves as a traditional quadrotor drone when in its vertical configuration and can rotate to a horizontal configuration, where it takes advantage of its wings to fly as a conventional aircraft. Modeling the dynamics of the tailsitter UAV and designing an autopilot controller is the main focus of this thesis. An adaptive controller was chosen for the tailsitter UAV due to its ability to modify the gains of the system based on the behavior of the vehicle to adapt to the unknown vehicle properties. This controller was validated using both computer simulations and actual flight tests. It was found that the adaptive controller was able to successfully control the transition between the vertical and horizontal flight regimes despite the uncertainties in the parameters of the vehicle.

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

In this dissertation, we present new approaches to improve standard designs in adaptive control theory, and novel adaptive control architectures. We first present a novel Kalman filter based approach for approximately enforcing a linear constraint in standard adaptive control design. One application is that this leads to alternative forms for well known modification terms such as e-modification. In addition, it leads to smaller tracking errors without incurring significant oscillations in the system response and without requiring high modification gain. We derive alternative forms of e- and adaptive loop recovery (ALR-) modifications. Next, we show how to use Kalman filter optimization to derive a novel adaptation law. This results in an optimization-based time-varying adaptation gain that reduces the need for adaptation gain tuning. A second major contribution of this dissertation is the development of a novel derivative-free, delayed weight update law for adaptive control. The assumption of constant unknown ideal weights is relaxed to the existence of time-varying weights, such that fast and possibly discontinuous variation in weights are allowed. This approach is particularly advantageous for applications to systems that can undergo a sudden change in dynamics, such as might be due to reconfiguration, deployment of a payload, docking, or structural damage, and for rejection of external disturbance processes. As a third and final contribution, we develop a novel approach for extending all the methods developed in this dissertation to the case of output feedback. The approach is developed only for the case of derivative-free adaptive control, and the extension of the other approaches developed previously for the state feedback case to output feedback is left as a future research topic. The proposed approaches of this dissertation are illustrated in both simulation and flight test.