ABSTRACT Bridgeless Active Power Factor Correction Using a Current Fed Push Pull Converter Jeramie Seth Bianchi Switched Mode Power Supplies have become increasingly popular for efficient methods of delivering power to an assortment of electronic devices. This thesis proposes a method of using a current fed push pull converter to provide active power factor correction and rectification in a single stage. While most AC-DC converters utilize a bridge rectifier to convert AC-DC and then perform DC-DC conversion, the proposed circuit will utilize its output diodes to perform rectification, thus eliminating the need for a bridge rectifier. This circuit will also inherently provide power factor correction because the input current has a continuous path for current flow due to the current fed topology where no time exists for both switches to be off. Through analog circuitry for the controller, multiple methods of AC main switching are tested, including isolation techniques using optocouplers, to prove the most efficient way to control a bidirectional switch. Simulations with PSPICE and hardware implementation of the design prove that alternative methods to provide quality power conversion for Switched Mode Power Supplies are available. Keywords: Active Power Factor Correction, Current Fed Push Pull Converter, SMPS, Bidirectional Switching, IGBT, Bridgeless Rectification

The focus of this document is on the design, modeling, and control of a self-balancing two wheel robot, hereafter referred to as the balance bot, driven by independent brushless DC (BLDC) motors. The balance bot frame is composed of stacked layers allowing a lightweight, modular, and rigid mechanical design. The robot is actuated by a pair of brushless DC motors equipped with Hall effect sensors and encoders allowing determination of the angle and angular velocity of each wheel. Absolute orientation measurement is accomplished using a full 9-axis IMU consisting of a 3-axis gyroscope, a 3-axis accelerometer, and a 3-axis magnetometer. The control algorithm is designed to minimize deviations from a set point specified by an external radio remote control, which allows the remote operator to steer and drive the bot wirelessly while it remains balanced. Multiple dynamic models are proposed in this analysis, and the selected model is used to develop a linear-quadratic regulator based state-feedback controller to perform reference tracking. Controller tracking performance is improved by incorporating a prefilter stage between the setpoint command from the remote control and the state-feedback controller. Modeling of the actuator dynamics is considered brie y and is discussed in relation to the control algorithm used to balance the robot. Electrical and software design implementations are also presented with a focus on effective implementation of the proposed control algorithms. Simulated and physical testing results show that the proposed balance bot and controller design are not only feasible but effective as a means of achieving robust performance under dynamic tracking profiles provided by the remote control.

On-board gimbal systems for camera stabilization in helicopters are typically based on linear models. Such models, however, are inaccurate due to system nonlinearities and complexities. As an alternative approach, artificial neural networks can provide a more accurate model of the gimbal system based on their non-linear mapping and generalization capabilities. This thesis investigates the applications of artificial neural networks to model the inertial characteristics (on the azimuth axis) of the inner gimbal in a gyro-stabilized multi-gimbal system. The neural network is trained with time-domain data obtained from gyro rate sensors of an actual camera system. The network performance is evaluated and compared with measured data and a traditional linear model. Computer simulation results show the neural network model fits well with the measured data and significantly outperforms a traditional model.

Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum system, it learns an effective policy up to three times faster than the other algorithms. In the cartpole system, it learns an effective policy up to nearly fifteen times faster than the other algorithms.

Robot path planning is a critical feature of autonomous systems. Rapidly-exploring Random Trees (RRT) is a path planning technique that randomly samples the robot configuration space to find a path between the start and end point. This thesis studies and compares the performance of four important RRT algorithms, namely, the original RRT, the optimal RRT (also termed RRT*), RRT*-Smart, and Informed RRT* for six different environments. The performance measures include the final path length (which is also the shortest path length found by each algorithm), time to find the first path, run time (of 1000 iterations) for each algorithm, total number of sampling nodes, and success rate (out of 100 runs). It is found that both RRT*-Smart and Informed RRT* algorithm result in shorter path lengths than the original RRT and RRT*. Typically, RRT*-Smart can find a suboptimal path in less number of iterations while the Informed RRT* is able to find the shortest path with increased number of iterations. On the other hand, the original RRT and RRT* are better suited for real-time applications as the Informed RRT* and RRT*-Smart have longer run time due to the additional steps in their processes.

The product of this thesis aims to enable the study of the biochemical and physical dynamics of the lower limbs at high levels of muscle tension and fast contraction speeds. This is accomplished in part by a magnetic resonance (MR) compatible ergometer designed to apply a load as a torque of up to 420 Nm acting against knee extension at speeds as high as 4.7 rad/s. The system can also be adapted to apply the load as a force of up to 1200 N acting against full leg extension. The ergometer is designed to enable the use of magnetic resonance spectroscopy and imaging in a three Tesla Siemens Skyra MRI system. Due to the electromagnetic limitations of having the device operate inside the magnet, the design is split into two components. One designed to fit inside the 70 cm bore of the scanner. This component is electromagnetically passive; made out of materials exhibiting minimal magnetic interference, and having no electrically powered parts. The other component is electromagnetically active; it contains all of the powered elements and actuates the passive part from another room. A tensioned cable transmits power through a waveguide; a pipe through the wall of the MRI room with an RF shield. The device was tested applying a sagittal plane moment on the knee joint during isometric, isokinetic, isotonic, and constant power contractions.

Attitude control remains one of the top engineering challenges faced by small satellite mission planning and design. Conventional methods for attitude control include propulsion, reaction wheels, magnetic torque coils, and passive stabilization mechanisms, such as permanent magnets that align with planetary magnetic fields. Drawbacks of these conventional attitude control methods for small satellites include size, power consumption, dependence on external magnetic fields, and lack of full control authority. This research investigates an alternative, novel approach to attitude-control method for small satellites, utilizing the noncommutative property of rigid body rotation sequences. Piezoelectric bimorph actuators are used to induce sinusoidal small-amplitude satellite oscillations on two of the satellites axes. While zero net change occurs on these signaled axes, the third axis can develop an average angular rate. This noncommutative attitude control methodology has several advantages over conventional methods, including scalability, power consumption, and operation outside of Earth's magnetic field. This research looks into the feasibility of such a system, and lays the foundation for a simple control system architecture.

Hysteresis current control has been widely used in power electronics with the advantages of fast dynamic response under parameter, line and load variation and ensured stability. However, a main disadvantage of hysteresis current control is the uncertain and varying switching frequency which makes it difficult to form an average-value model. The changing switching frequency and unspecified switching duty cycle make conventional average-value models based on PWM control difficult to apply directly to converters that are controlled by hysteresis current control. In this work, a new method for average-value modeling of hysteresis current control in boost converters, three-phase inverters, and brushless dc motor drives is proposed. It incorporates a slew-rate limitation on the inductor current that occurs naturally in the circuit during large system transients. This new method is compared with existing methods in terms of simulation run time and rms error. The performance is evaluated based on a variety of scenarios, and the simulation results are compared with the results of detailed models. The simulation results show that the proposed model represents the detailed model well and is faster and more accurate than existing methods. The slew-rate limitation model of hysteresis current control accurately captures the salient detail of converter performance while maintaining the computational efficiency of average-value models. Validations in hardware are also presented.

As electronics become smaller and more capable, it has become possible to conduct meaningful and sophisticated satellite missions in a small form factor. However, the capability of small satellites and the range of possible applications are limited by the capabilities of several technologies, including attitude determination and control systems. This dissertation evaluates the use of image-based visual attitude propagation as a compliment or alternative to other attitude determination technologies that are suitable for miniature satellites. The concept lies in using miniature cameras to track image features across frames and extracting the underlying rotation. The problem of visual attitude propagation as a small satellite attitude determination system is addressed from several aspects: related work, algorithm design, hardware and performance evaluation, possible applications, and on-orbit experimentation. These areas of consideration reflect the organization of this dissertation. A “stellar gyroscope” is developed, which is a visual star-based attitude propagator that uses relative motion of stars in an imager’s field of view to infer the attitude changes. The device generates spacecraft relative attitude estimates in three degrees of freedom. Algorithms to perform the star detection, correspondence, and attitude propagation are presented. The Random Sample Consensus (RANSAC) approach is applied to the correspondence problem to successfully pair stars across frames while mitigating false-positive and false-negative star detections. This approach provides tolerance to the noise levels expected in using miniature optics and no baffling, and the noise caused by radiation dose on orbit. The hardware design and algorithms are validated using test images of the night sky. The application of the stellar gyroscope as part of a CubeSat attitude determination and control system is described. The stellar gyroscope is used to augment a MEMS gyroscope attitude propagation algorithm to minimize drift in the absence of an absolute attitude sensor. The stellar gyroscope is a technology demonstration experiment on KySat-2, a 1-Unit CubeSat being developed in Kentucky that is in line to launch with the NASA ELaNa CubeSat Launch Initiative. It has also been adopted by industry as a sensor for CubeSat Attitude Determination and Control Systems (ADCS).

The problem of aircraft conflict detection and resolution (CDR) in uncertainty is addressed in this thesis. The main goal in CDR is to provide safety for the aircraft while minimizing their fuel consumption and flight delays. In reality, a high degree of uncertainty can exist in certain aircraft-aircraft encounters especially in cases where aircraft do not have the capabilities to communicate with each other. Through the use of a probabilistic approach and a multiple model (MM) trajectory information processing framework, this uncertainty can be effectively handled. For conflict detection, a randomized Monte Carlo (MC) algorithm is used to accurately detect conflicts, and, if a conflict is detected, a conflict resolution algorithm is run that utilizes a sequential list Viterbi algorithm. This thesis presents the MM CDR method and a comprehensive MC simulation and performance evaluation study that demonstrates its capabilities and efficiency.