Control allocation is examined for linear time-invariant problems that have more controls than degrees of freedom. The controls are part of a physical system and are subject to limits on their maximum positions. A control allocation scheme commands control deflections in response to some desired output. The ability of a control allocation scheme to produce the desired output without violating the physical position constraints is used to compare allocation schemes. Methods are developed for computing the range of output for which a given scheme will allocate admissible controls. This range of output is expressed as a volume in the n-dimensional output space. The allocation schemes which are detailed include traditional allocation methods such as Generalized Inverse solutions as well as more recently developed methods such as Daisy Chaining, Cascading Generalized Inverses, Null-Space Intersection methods, and Direct Allocation. Non-linear time-varying problems are analyzed and a method of control allocation is developed that uses Direct Allocation applied to locally linear problems to allocate the controls. This method allocates controls that do not violate the position limits or the rate limits for all the desired outputs that the controls are capable of producing. The errors produced by the non-linearities are examined and compared with the errors produced by globally linear methods. The ability to use the redundancy of the controls to optimize some function of the controls is explored and detailed. Additionally, a method to reconfigure the controls in the event of a control failure is described and examined. Detailed examples are included throughout, primarily applying the control allocation methods to an F-18 fighter with seven independent moment generators controlling three independent moments and the F-18 High Angle of Attack Research Vehicle (HARV) with ten independent moment generators. / Ph. D.

This study examines the "off-the-job" behavior of individuals in office-level leadership positions across the Big 4 audit firms in the U.S. In their leadership role, the managing partner is responsible for setting the tone at the top of an office through formal communication of firm-wide policies and an informal example through their behavior and preferences. Given this role, I predict that engagements conducted within offices led by individuals who are willing to break the rules will exhibit characteristics synonymous with increased audit risks. Relying on their history of legal infractions to identify rule-breaking behavior, I find managing partners with prior infractions are associated with engagements that reflect increased misstatement risk and detection risk (i.e., lower auditor effort). Additional tests reveal that the results are concentrated in offices that are located further away from alternative governance mechanisms within the same audit firm. Importantly, after controlling for the risk of misstatement, I find the pricing of misstatement risk declines significantly on engagements in offices with infraction managing partners. The results are robust to alternative measures of managing partners' prior infractions and the use of entropy balancing techniques, along with several other robustness tests. Collectively, my study contributes to our limited knowledge of the quality control structures in place at large audit firms and provides a potential mechanism for tone at the Big 4 audit firms to vary across offices. / Doctor of Philosophy / In their leadership role, office managing partners are the "top executive" appointed to lead the Big 4 audit offices across the U.S. While audit firms have reputation and litigation incentives to provide high quality audits, these incentives do not necessarily apply to individual auditors. Therefore, audit firms are required to formalize a system of quality controls—including leadership and tone at the top—to ensure promote professional skepticism, stress quality service, and reduce overall audit risk on engagements. Relatedly, during inspections, the PCAOB examines whether the actions and communications by managing partners in local leadership positions demonstrate a commitment to audit quality and compliance with applicable regulations and professional standards. Grounded in revealed preference theory, I rely on a managing partner's history of legal infractions to identify offices led by partners with impulsive, risk-taking, and present-oriented personalities. Criminology and psychology research empirically validate the cross-situational consistency of individual behavior and decisions over time and in different settings. In other words, individuals who commit legal infractions—including less severe traffic violations such as parking tickets—exhibit a preference or propensity to break the rules. To the extent that an individual's leadership style is influenced by their personal ethics, values, and attitudes, I expect variation in a managing partner's history of legal infraction to reflect variation in their leadership style and office tone towards audit risk on engagements. Consistent with this prediction, I find managing partners with prior infractions are associated with engagements that reflect increased misstatement risk and detection risk (i.e., lower auditor effort). Additional tests reveal that the results are concentrated in offices that are located further away from alternative governance mechanisms within the same audit firm. Importantly, after controlling for the risk of misstatement, I find the pricing of misstatement risk declines significantly on engagements in offices with infraction managing partners. The results are robust to alternative measures of managing partners' prior infractions and the use of entropy balancing techniques, along with several other robustness tests. Collectively, my study contributes to our limited knowledge of the quality control structures in place at large audit firms and provides a potential mechanism for tone at the Big 4 audit firms to vary across offices.

A new method for small-signal analysis of switching power converters is developed and implemented in a computer program. The method is derived for systems where the nonlinearities can be described by elements that can take on one of two values, based upon a controlling variable. Another requirement is that the system be periodic. The method is shown by examples to be very accurate, even at high frequencies. It predicts the subharmonic oscillation that can occur in converters with constant-frequency current-mode control. It is implemented using the COSMIR program to solve for the state equations and for steady-state, making a general power supply simulation program. / Ph. D.

This thesis aims at estimating vital vehicle states and developing control algorithms for automotive suspensions and vehicle stability. A parametric model of an automotive monotube damper is developed and several control algorithms for semi-active suspensions have been developed. An extensive comparison of different control algorithms has been done. Skyhook, Groundhook, Hybrid, Acceleration-driven, Power-driven, Groundhook-linear, Linear Quadratic Regulator (LQR) optimal, Genetic algorithm optimized Linear Quadratic Regulator optimal, Model-reference adaptive, H∞ robust, µ-synthesis, fuzzy-logic based, and Deep Reinforcement learning based control algorithms have been developed and simulated. A shock dyno is instrumented and skyhook and groundhook control algorithms have been implemented as well. In addition to this, a semi-active suspension switching based control algorithm is developed for reducing the effort of a direct moment yaw rate controller, and improve stability of a vehicle when turning. Accelerometer based intelligent tires have been used to estimate vehicle states like vertical load on tire, velocity of the vehicle, unsprung mass acceleration, and forces on a tire. All these estimations would be helpful in observing various parameters of a vehicle using data from only a tri-axis accelerometer inside the tire. Data was collected in an instrumented Volkswagen Jetta and a Trailer setup as well. The test vehicle was instrumented with a tri-axis accelerometer inside the tire, encoder, Inertial Measurement Unit (IMU), and VBOX Racelogic Global Positioning System (GPS) based velocity measurement unit. For payload estimation, the data collected by the in-tire accelerometer was converted into frequency domain using Welch's method of averaging, followed by feature extraction. The extracted features were fed to a trained bagged trees model. Root mean squared error of 11% was observed on the test dataset. For velocity estimation, the data collected by the accelerometer was fed to a variational mode decomposition process. The extracted mode was converted to time-frequency domain using Hilbert transform and features for machine learning were extracted. A root mean squared error of 1.02kmph was observed on the trained dataset. A Gaussian process model was trained for this application. For unsprung mass acceleration estimation, the test vehicle was instrumented with an accelerometer near the wheel spindle as well. For this estimation problem, Convolutional neural networks (CNN) were used. The time-frequency spectrogram of x, y, and z axis data of the in-tire accelerometer were considered as the three color channels of an image. With this, an image of 224 x 224 x 3 dimensions was generated, which represented the time and frequency variation of data. These images were used for training the CNN and a 96.8% coefficient of correlation was obtained for this regression task. For the last wheel force estimation problem, the concept of training the images generated by overlapping time-frequency matrices was used and an accuracy of 90.1% was achieved. With these estimation of vehicle states, better control algorithms can be developed and deployed for better handling, safety and comfort of vehicles using data from only tri-axis accelerometer in the tire. / Master of Science / The main objective of this thesis is to aid in the development of better control systems for vehicles, using data from accelerometer-based intelligent tire. Payload on the vehicle's tire, vehicle velocity, wheel acceleration, and wheel forces are vital parameters, which if estimated correctly can be instrumental in having better understanding of the vehicle's condition. A tri-axis accelerometer is mounted inside the tire, and is used for estimating these vehicle parameters. Statistical models are developed based on features extracted from the accelerometer data. The main challenge was to use the data collected by only intelligent tire to estimate vehicle states. This makes the developed algorithms independent of other sensors and hence economic. Tires are the only component which serve as a link between the vehicle and road. Hence, these parameter estimations can be accurately observed simultaneously using the in-tire accelerometer data. Testing is done on an instrumented trailer-test setup and a Volkswagen Jetta. The vehicle is instrumented with the intelligent tire, a Global positioning system (GPS) based velocity measuring unit, Inertial measurement unit (IMU), and encoder. Testing is done for different loading conditions, road surfaces, inflation pressures, and vehicle velocities. In this way, it has been attempted to make the developed statistical models robust and expose them to a multitude of test conditions. In addition to this, several suspension semi-active control algorithms have been developed for improving vehicle ride comfort and road holding. A parametric damper model has been developed, and several control algorithms have been simulated. A shock dyno experimental setup has been instrumented and some of the control algorithms have been implemented. With this, several suspension semi-active control algorithms have been developed, and statistical models have been developed for estimation of various vehicle parameters. This research can be helpful for developing accurate control algorithms for active safety systems in a vehicle.

The goal of this thesis is to investigate automatic mass balancing methods for spacecraft attitude dynamics simulators, create a hardware design for a mass balancing system, and assemble the hardware on the Cal Poly Spacecraft Attitude Dynamics Simulator (SADS). Spacecraft attitude dynamics simulators replicate the torque-free environment of space with ground-based hardware. The SADS is mounted on a spherical air bearing, and includes a pyramid of four reaction wheels. The air bearing allows frictionless, unbounded rotation about the vertical axis, and 30 degrees about the horizontal axes. The torque-free configuration of the SADS can be used to test spacecraft attitude control software and hardware. For spacecraft attitude simulators, it is essential to accurately align the center of mass with the center of rotation. Any alignment error will cause a gravitational disturbance torque that quickly saturates actuators. It is necessary to use an automatic mass balancing system if the system's hardware is regularly adjusted, since manual mass balancing methods become prohibitively time consuming. Once balanced, it is also useful if the attitude simulator can measure its inertia tensor for use in control software. To avoid displacing the center of mass, a symmetrical six-sliding-mass balancing system was developed for the SADS, driven by geared DC motors. Several mass balancing algorithms were designed that do not require additional actuators. These algorithms enable mass balancing prior to the integration of reaction wheels, and are intended to eventually be part of a two-stage balancing approach. In the controlled stage, a continuous time controller corrects the gravitational disturbance torque. A moment distribution law for the sliding masses is then used with an FSFB or adaptive controller to align the center of mass and rotation. In the system identification stage, angles and angular rates are measured during phased torque sine sweeps so that the inertia tensor and center of mass position can be estimated. After making a correction to the sliding mass positions, the identification and correction process is repeated until the system is balanced. To be considered 'balanced,' any remaining disturbance torques must be negligible on the time scale of a control system simulation. The horizontal axis positional errors for the center of mass should be on the order of one tenth of a millimeter, and it is generally acceptable for the vertical axis error to be approximately an order of magnitude greater. Since a vertical center of mass offset is generally aligned with the structure of the air bearing, its corresponding gravity disturbance torque is reduced. Since the mass properties of the SADS must be measured regardless, they are a convenient metric for the effectiveness of the balancing system.

The transmission line is an underappreciated style of loudspeaker enclosure characterized by an acoustic labyrinth stemming from the rear of the speaker driver. In practice, the transmission line enclosure produces airy sound uncharacteristic of other styles, at the cost of more pronounced resonant peaks. The most important practical drawback of this loudspeaker enclosure design is the difficulty of properly applying damping to these enclosures. Ideally, this difficulty can be mitigated using an analytical model that accurately predicts the SPL frequency response of a transmission line loudspeaker system for a given geometry and mass of damping material. This research takes the first step towards establishing such a model by developing a limited model for a simple enclosure geometry. Through the application of a modal analysis, this research predicts the frequency response of the enclosure for the first five modes, discusses the effect damping has on this response, and experimentally verifies the produced outputs. For the simplified transmission line enclosure, the developed model successfully predicts the target portion of the frequency response. The model produces accurate results for a range of damping levels using experimentally derived damping ratios for the first five modes. The resulting curves for each modal damping ratio allow for a set of novel damping ratios to be produced from an input mass of damping material. Through this process, an input mass of damping material produces the predicted frequency response for a straight, non-tapered transmission line enclosure. This prediction can make damping a transmission line enclosure much more efficient, allowing for transmission line loudspeakers to be more widely available.

Yes / This paper investigates whether shareholder value is affected by non-compliance with the prescriptions of a principle-based ‘comply or explain’ sys-tem of corporate governance in the context of the global financial crisis of2007–2009. Using System Generalized Method of Moments estimates to controlfor different types of endogeneity, the main findings of this paper suggest thatnon-compliance with the UK Corporate Governance Code adversely affectsshareholder value. Furthermore, ex-post estimates reveal that compliance withcertain corporate governance mechanisms is more beneficial than others. Withregard to this, compliance with provisions related to board independence ismore important than complying with performance-related pay requirements ofthe code. These findings have implications for policy makers and financialinstitutions regarding the usefulness of compliance with a prescribed code ofcorporate governance, specifically during periods of financial distress.

Artificial Neural Networks (ANNs) have proved to be useful in approximating non- linear systems in many applications including motion control. ANNs advocated in switched reluctance motor (SRM) control typically have a large number of neurons and several layers which impedes their real time implementation in embedded sys- tems. Real time estimation at high speeds using these ANNs is diffcult due to the high number of operations required to process the ANN controller. An insuffcient availability of time between two sampling intervals limits the available computation time for both processing the neural net and the other functions required for the motor drive. One ideal application of ANNs in SRM control is rotor position estimation. Due to reliability issues, elimination of the rotor position sensors is absolutely required for high volume, high speed and low cost applications of SRM's. ANNs provide a means by which drive designers can implement position sensorless drive technology that is both robust and easily implemented. It is demonstrated that a new and novel ANN configuration can be implemented for accurate rotor position estimation in a sensorless SRM drive. Consisting of just 4 neurons, the neural estimator is the smallest of its kind for SRM rotor position estimation. The breakthrough that provided the reduction was the addition of a non- linear input. Typical input spaces for SRM position neural estimators consist of both current,and fux-linkage. The neural network was trained on-line using these inputs and a third, non-linear input provided by a preprocessed product of the two typical inputs. / Master of Science

Piezoelectric actuators frequently exhibit a time-dependent behavioral phenomenon known as hysteresis, resulting in a lag in the deformation of the actuator compared to linear models. The presence of hysteresis complicates control systems involving piezoelectric actuators. However, traditional modeling methods for piezoelectric actuated smart structures often treat the piezoelectric patches as linear actuators without considering hysteresis, leading to suboptimal controller performance. This thesis aims to establish a comprehensive model by integrating the Euler-Bernoulli beam bending model with the hysteresis dynamics induced by two opposing piezoelectric patches attached to a beam. A model expansion method is employed to transform the partial differential equations describing beam vibration into a set of ordinary differential equations in the modal coordinate frame. These equations are then coupled with the Bouc-Wen model describing the hysteresis of piezoelectric materials. Model parameters are identified using a genetic algorithm tested against experimental data across varied excitation frequencies. The experimental dataset is divided into two sets: a training set for the genetic algorithm and a validation set to verify the identified model. Results demonstrate that the inclusion of hysteresis in a nonlinear model provides better agreement with experimental results than the linear model, thereby enhancing the predictive capability of piezoelectric actuator behavior. This thesis has laid the foundation for future work on advanced control methods to mitigate beam vibration under external excitation, thus optimizing smart structure performance.

The ever increasing traffic congestion being experienced on urban freeways has caused attention to be focused on methods for improving traffic operations on these facilities. The reduction or elimination of freeway congestion can be handled in two basic ways: 1) by increasing the freeway's capacity defined as the number of vehicles per unit time that can pass a given point in one lane of the freeway multiplied by the number of lanes on the freeway, or 2) by decreasing the traffic loads imposed on these freeways. Monumental costs and adverse public relations are involved in the construction of either new freeways or additional lanes in urban areas to increase freeway capacity. Due to these factors, the move in recent years has been toward decreasing traffic loads to accomplish the goals of reduction or elimination of freeway congestion. In an effort to combat the freeway congestion, traffic researchers have devoted much time and effort to the theoretical and practical aspects of systems in which controlled access is utilized to prevent or reduce congestion caused by traffic demands in excess of freeway operational capacity. Such systems perform the function of 'freeway surveillance and control'. An important aspect of these systems is the control mechanism used for the regulation of the ramp traffic to the freeway. This topic of ramp metering is the subject of this report.