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A novel noise control system is developed using adaptable tuned vibration absorbers (ATVA) to interact with a vibrating host structure in such a way as to reduce radiated acoustic energy. ATVA's are single-degree-of-freedom resonant devices that can change their resonant frequency and damping over a range. This ATVA noise control system is targeted at applications with tonal disturbances such as propeller aircraft. The motivation for this work is to better understand and experimentally demonstrate the noise control performance of globally detuned vibration absorbers (i.e. tuned away from the disturbance) compared to that of perfectly tuned devices on complex structures. A two-tier hierarchical control approach is used where a global control algorithm attempts to minimize a global parameter such as radiated acoustic energy by directing the adaptation of subordinate ATVA's. The global control algorithm uses an adaptive simplex search algorithm that requires no initial knowledge of the structure or the ATVA's. The ATVA's also require no model of the structure, each utilizing only the local vibration of its own mass and control gains set by the global controller. Noise control using a single ATVA is first studied on a small simply supported plate. Then, a multiple ATVA system is tested on a large plate structure at several test frequencies where many structural modes participate. Noise reductions up to 22 dB are achieved at locations in the radiated field. Further, it is found in some cases, classic tuning of the ATVA results in increased structural noise radiation. ATVA's are realized by outfitting typical inertial (proof-mass) actuators with a classical feedback loop. The device's resonant frequency and damping can be controlled independently, yet simultaneously via two control gains. The ATVA's are designed, built, and characterized for their adaptable domain and power requirements. A cohesive analytical model of the ATVA is also developed and used to compliment the experimental results. / Ph. D.

Painting still plays a fundamental role in communication nowadays. For example, the paint on the road, called road surface marking, guides the traffic in order and maintains the high efficiency of the entire modern traffic system. With the development of the Autonomous Ground Vehicle (AGV), the idea of a line Painting Robot emerged. In this thesis, a Painting Robot was designed as a standalone system based on the AGV platform. In this study, the mechanical and electronic design of a Painting Robot was discussed. The overall design was to fulfill the requirements of the line painting. Computer vision techniques were applied to this thesis since the camera was selected as the major sensor of the robot. Advanced control theory was introduced to this thesis as well. Three different controllers were developed. The Proportional-Integral (PI) controller with an anti-windup feature was designed to overcome the drawbacks of the traditional PI controller. Model Reference Adaptive Control (MRAC) was introduced into this thesis to deal with the uncertainties of the system. At last, the hybrid PI-MRAC controller was implemented to maintain the advantages of both PI and MRAC approaches. Experiments were conducted to evaluate the performance of the entire system, which indicated the successful design of the Painting Robot. / Master of Science / Painting still plays a fundamental role in communication nowadays. With the development of the Autonomous Ground Vehicle (AGV), the idea of a line Painting Robot emerged. In this thesis, a Painting Robot was designed as a standalone system based on the AGV platform. In this study, a Painting Robot with a two-camera system was designed. Computer vision techniques and advanced control theory were introduced into this thesis. Three different controllers were developed, including Proportional-Integral (PI) with an anti-windup feature, Model Reference Adaptive Control (MRAC) and the hybrid PI-MRAC. Experiments were conducted to evaluate the performance of the entire system, which indicated the successful design of the Painting Robot.

With an increased push for commercial autonomous cars, the demand of high speed systems capable of performing in unstructured driving environments is growing. In this thesis, the behavior of a bio-inspired predator prey model is considered to stimulate a more organic response to obstacles and a moving target than existing algorithms. However, the current predator prey model has a disconnect between the desired velocities commanded and the torque signals provided to the motors due the dynamics of the vehicle not accounted for. This causes the vehicle to derail from its intended trajectory at sharp turns. In this study, we start by adding dynamic behavior to the unicycle model to account for the varying dynamics of the vehicle. A backstepping algorithm is developed to connect the predator-prey model commanding desired velocities to an appropriate torque controller for the motors of the vehicle. To account for the unknown dynamic model parameters an adaptive control approach is utilized. Three different controllers are developed and evaluated. Out of the three, the indirect MRAC backstepping controller is deemed unsuitable due to its limitations with handling unknown parameter structure. The direct MRAC backstepping is deemed suitable and therefore simulated and implemented on the vehicle. The newly derived controller is able to overcome the disconnect and allow the vehicle to optimally track its trajectory for a velocity range of 1 m/s to 9 m/s despite varying dynamics. Lastly, the L1 adaptive backstepping controller is introduced and simulated to provide an alternative, more robust solution to the direct MRAC backstepping controller. / Master of Science

Traditional flight control design is based on linearization of the equations of motion around a set of trim points and scheduling gains of linear (optimal) controllers around each of these points to meet performance specifications. For high angle of attack maneuvers and other aggressive flight regimes (required for fighter aircraft for example), the dynamic nonlinearities are dependent not only on the states of the system, but also on the control inputs. Hence, the conventional linearization-based logic cannot be straightforwardly extended to these flight regimes, and non-conventional approaches are required to extend the flight envelope beyond the one achievable by gain-scheduled controllers. Due to the nonlinear-in-control nature of the dynamical system in aggressive flight maneuvers, well-known dynamic inversion methods cannot be applied to determine the explicit form of the control law. Additionally, the aerodynamic uncertainties, typical for such regimes, are poorly modelled, and therefore there is a great need for adaptive control methods to compensate for dynamic instabilities. In this thesis, we present an adaptive control design method for both short-period and lateral/directional control of a fighter aircraft. The approach uses a specialized set of radial basis function approximators and Lyapunov-based adaptive laws to estimate the unknown nonlinearities. The adaptive controller is defined as a solution of fast dynamics, which verifies the assumptions of Tikhonov's theorem from singular perturbations theory. Simulations illustrate the theoretical findings. / Master of Science

This thesis details the implementation of two adaptive controllers on autonomous underwater vehicle(AUV) attitude dynamics starting from the standard six degree-of-freedom dynamic model. I apply two model reference adaptive control (MRAC) algorithms which make use of kernel functions for learning functional uncertainty present in the system dynamics. The first method extends recent results on model reference adaptive control using reproducing kernel Hilbert space (RKHS) learning techniques for some general cases of multi-input systems. The first controller design is a model reference adaptive controller (MRAC) based on a vector- valued RKHS that is induced by operator-valued kernels. This paper formulates a model reference adaptive control strategy based on a dead zone robust modification, and derives conditions for the ultimate boundedness of the tracking error in this case. The second controller is an implementation of the Gaussian Process MRAC developed by Chowdhary, et al. I discuss the method of each of these algorithms before contrasting the underlying theoretical structure of each algorithm. Finally, I provide a comparison of each algorithm's performance on the six degree-of-freedom dynamic model of the Virginia Tech 690 AUV and provide field trial results for the RKHS based MRAC implementation. / Master of Science / This thesis details the implementation of two algorithms which control the attitude of an autonomous underwater vehicle. Rather than developing detailed dynamic models of the vehicles as is performed in classical control methods, each of these implementations only makes assumptions that the unknown portions of the dynamic models can be represented by a broad class of functions defined by a mathematical structure called a reproducing kernel Hilbert Space. Each algorithm implements learning techniques using the theory of reproducing kernel Hilbert spaces to bound the error between the vehicle attitude and the commanded vehicle attitude. One algorithm, called RKHS MRAC, implements an adaptive update law based on the attitude error to improve the controller performance. The second algorithm, called GP MRAC, uses estimated vehicle rotational accelerations and statistical learning methods to approximate the unknown function. Each of these methods is compared in theory and in a vehicle simulation. The RKHS MRAC is additionally demonstrated in field trial results.

Aerospace systems such as aircraft or missiles are subject to environmental and dynamical uncertainties. These uncertainties can alter the performance and stability of these systems. Adaptive control offers a useful means for controlling systems in the presence of uncertainties. However, very often adaptive controllers require more control effort than the actuator limits allow. In this thesis the original work of others on single input single output adaptive control in the presence of actuator amplitude limits is extended to multi-input systems. The Lyapunov based stability analysis is presented. Finally, the resultant technique is applied to aircraft and missile longitudinal motion. Simulation results show satisfactory tracking of the states of modified reference system. / Master of Science

The objective of adaptive control is to design a controller that can adjust its behaviour to tolerate uncertain or time-varying parameters. An adaptive controller typically consists of a linear time-invariant (LTI) compensator together with a tuning mechanism which adjusts the compensator parameters and yields a nonlinear controller. Because of the nonlinearity, the transient closed-loop behaviour is often poor and the control signal may become unduly large. Although the initial objective of adaptive control was to deal with time-varying plant parameters, most classical adaptive controllers cannot handle rapidly changing parameters. Recently, the use of a linear periodic (LP) controller has been proposed as a new approach in the field of model reference adaptive control [1]. In this new approach, instead of estimating plant parameters, the “ideal control signal” (what the control signal would be if the plant parameters and states were measurable) is estimated. The resulting controller has a number of desirable features: (1) it handles rapid changes in the plant parameters, (2) it provides nice transient behaviour of the closed-loop system, (3) it guarantees that the effect of the initial conditions declines to zero exponentially, and (4) it generates control signals which are modest in size. Although the linear periodic controller (LPC) has the above advantages, it has some imperfections. In order to achieve the desirable features, a rapidly varying control signal and a small sampling period are used. The rapidly time-varying control signal requires fast actuators which may not be practical. The second weakness of the LPC [1] is poor noise rejection behaviour. The small sampling period results in large controller gains and correspondingly poor noise sensitivity, since there is a clear trade-off between tracking and noise tolerance. As the last drawback, this controller requires knowledge of the exact plant relative degree. Here we extend this work in several directions: (i) In [1], the infinity-norm is used to measure the signal size. Here we redesign the controller to yield a new version which provides comparable results when the more common 2-norm is used to measure signal size, (ii) A key drawback of the controller of [1] is that the control signal moves rapidly. Here we redesign the control law to significantly alleviate this problem, (iii) The redesigned controller can handle large parameter variation and in the case that the sign of high frequency gain is known, the closed-loop system is remarkably noise-tolerant, (iv) We prove that in an important special case, we can replace the requirement of knowledge of the exact relative degree with that of an upper bound on the relative degree, at least from the point of view of providing stability, and (v) A number of approaches to improve the noise behaviour of the controller are presented. Reference: [1] D. E. Miller, “A New Approach to Model Reference Adaptive Control”, IEEE Transaction on Automatic Control, Vol. 48, No. 5, pages 743-756, May 2003.