This paper explores the noise shaping and noise producing qualities of Delta-Sigma Modulators (DSM) and Pulse-Width Modulators (PWM). DSM has long been dominant in the Delta Sigma Analog-to-Digital Converter (DSADC) as a noise-shaped quantizer and time discretizer, while PWM, with a similar self oscillating structure, has seen use in Class D Power Amplifiers, performing a similar function. It has been shown that the PWM in Class D Amplifiers outperforms the DSM [1], but could this advantage be used in DSADC use-cases? LTSpice simulation and printed circuit board implementation and test are used to present data on four variations of these modulators: The DSM, PWM, the out-of-loop discretized PWM (OOLDP), and the cascaded modulator. A generic form of an Nth order loop filter is presented, where three orders of this generic topology are analyzed in simulation for each modulator, and two orders are used in physical testing.

Smart structure control systems commonly use piezoceramic sensors or accelerometers as vibration measurement devices. These measurement devices often produce noisy and/or low-precision signals, which makes it difficult to measure small-amplitude vibrations. Laser interferometry devices pose as an alternative high-precision position measurement method, capable of nanometer-scale resolution. The aim of this research is to utilize a model-based design approach to develop and implement a real-time Linear Quadratic Gaussian (LQG) regulator for a piezoelectric actuated smart structure using a high-precision laser interferometry measurement system to suppress the excitation of vibratory modes. The analytical model of the smart structure is derived using the extended Hamilton Principle and Euler-Bernoulli beam theory, and the equations of motion for the system are constructed using the assumed-modes method. The analytical model is organized in state-space form, in which the effects of a low-pass filter and sampling of the digital control system are also accounted for. The analytical model is subsequently validated against a finite-element model in Abaqus, a lumped parameter model in Simscape Multibody, and experimental modal analysis using the physical system. A discrete-time proportional-derivative (PD) controller is designed in a heuristic fashion to serve as a baseline performance criterion for the LQG regulator. The Kalman Filter observer and Linear Quadratic Regulator (LQR) components of the LQG regulator are also derived from the state-space model. It is found that the behavior of the analytical model closely matches that of the physical system, and the performance of the LQG regulator exceeds that of the PD controller. The LQG regulator demonstrated quality estimation of the state variables of the system and further constitutes an exceptional closed-loop control system for active vibration control and disturbance rejection of the smart structure.

Electric and hybrid-electric vehicles lean heavily on intricate control algorithms to provide smooth, reliable, and secure operations under any driving conditions. Three distinct supervisory control strategies have been developed, each aiming to improve reliability and vehicle performance of a dual-motor electric vehicle equipped with an all-wheel-drive, fully electric powertrain. These algorithms are adept at dynamically modulating and constraining the torque provided to the wheels, leveraging two autonomous permanent magnet electric drive units. This study utilizes a vehicle model jointly provided by MathWorks and General Motors in partnership with industry sponsors. The these strategies were implemented in the model and enhanced the performance, vehicle range, energy consumption, regenerated energy using specific EDUs provided by sponsors. Adhering to a systematic engineering iterative method, the emphasis was heavily placed on simulation and modeling during the development and validation of these strategies. Simulations ensured robust testing before field implementation, emphasizing software modeling's vital role.

This paper describes the design, manufacturing, and testing of a novel controllable hypoxic incubator with fully functional oxygen gas control and temperature control in a humid environment. On the current market, a majority of the few hypoxic incubators use pre-mixed gas that does not offer precise control over gas concentration. The objective for this project was to create a chamber that allows the user to set the O2 concentration to varying set points of % O2 while maintaining the chamber at a constant body temperature, CO2 level, humidity, and sterility. To start the project, multiple concepts were developed for the chamber design and the control system. These concepts were compared against developed engineering specs and were evaluated amongst the team and sponsor. From there, a detailed CAD model was developed and utilized to design the structure and was used as a guide for manufacturing. The control system was prototyped on breadboards via Arduino. This breadboard testing served as the map to solder perf boards, which are utilized as the final structure for the control system. Once all parts were sourced, machined, and assembled for the final chamber and the control system, these subassemblies were integrated together with a regulated gas system via various tubing. The integrated final design underwent a variety of testing to validate the incubator design and control system. Testing was performed throughout the course of this project: material testing, gas leak testing, cell test, temperature control test, and gas control system optimization; however, the most important of these tests were those relating to the environmental control of the incubator. These tests confirmed whether the incubator design was functional as a practical incubator. Testing confirmed that O2 and temperature control maintained in spec over a short and long period of time while maintaining a humid environment. CO2 control optimization had more complications than the O2 hypoxia system. During testing CO2 concentration would typically overshoot the set point, likely due to a lack of precise control over the gas flow. CO2 variability was reduced due to optimization in the code, but not fully mitigated. Future iterations of this chamber could improve upon the CO2 control and streamline the user interface.

The development of a numeric simulation for predicting the performance of an Ocean Current Energy Conversion System is presented in this thesis along with a control system development using a PID controller for the achievement of specified rotational velocity set-points. In the beginning, this numeric model is implemented in MATLAB/Simulink® and it is used to predict the performance of a three phase squirrel single-cage type induction motor/generator in two different cases. The first case is a small 3 meter rotor diameter, 20 kW ocean current turbine with fixed pitch blades, and the second case a 20 meter, 720 kW ocean current turbine with variable pitch blades. Furthermore, the second case is also used for the development of a Voltage Source Variable Frequency Drive for the induction motor/generator. Comparison among the Variable Frequency Drive and a simplified model is applied. Finally, the simulation is also used to estimate the average electric power generation from the 720 kW Ocean Current Energy Conversion System which consists of an induction generator and an ocean current turbine connected with a shaft which modeled as a mechanical vibration system.

This thesis describes a genetic approach for shaping the dynamic responses of linear state space systems through pole placement. This paper makes further comparisons between this approach and an island approach parallel genetic algorithm (PGA) which incorporates orthogonal mutation vectors to increase sub-population specialization and decrease convergence time. Both approaches generate a gain vector K. The vector K is used in state feedback for altering the poles of the system so as to meet step response requirements such as settling time and percent overshoot. To obtain the gain vector K by the proposed genetic approaches, a pair of ideal, desired poles is calculate first. Those poles serve as the basis by which an initial population is created. In the island approach, those poles serve as a basis for n populations, where n is the dimension of the necessary K vector. Each member of the population is tested for its fitness (the degree to which it matches the criteria). A new population is created each “generation” from the results of the previous iteration, until the criteria are met, or a certain number of generations have passed. Several case studies are provided in this paper to illustrate that this new approach is working, and also to compare performance of the two approaches.