Engineering Commons^™

Model-Based Design Of An Optimal Lqg Regulator For A Piezoelectric Actuated Smart Structure Using A High-Precision Laser Interferometry Measurement System, Grant P. Gallagher

Master's Theses

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 …

Optimal Direct Yaw Moment Control Of A 4wd Electric Vehicle, Winston James Wight

Master's Theses

This thesis is concerned with electronic stability of an all-wheel drive electric vehicle with independent motors mounted in each wheel. The additional controllability and speed permitted using independent motors can be exploited to improve the handling and stability of electric vehicles. In this thesis, these improvements arise from employing a direct yaw moment control (DYC) system that seeks to adapt the understeer gradient of the vehicle and achieve neutral steer by employing a supervisory controller and simultaneously tracking an ideal yaw rate and ideal sideslip angle. DYC enhances vehicle stability by generating a corrective yaw moment realized by a torque …

Utilizing Trajectory Optimization In The Training Of Neural Network Controllers, Nicholas Kimball

Master's Theses

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 …

Optimizing Control Of Shell Eco-Marathon Prototype Vehicle To Minimize Fuel Consumption, Chad Louis Bickel

Master's Theses

Every year the automotive industry strives to increase fuel efficiency in vehicles. When most vehicles are designed, fuel efficiency cannot always come first. The Shell Eco-marathon changes that by challenging students everywhere to develop the most fuel-efficient vehicle possible. There are many different factors that affect fuel efficiency, and different teams focus on different vehicle parameters. Currently, there is no straightforward design tool that can be used to help in Shell Eco-marathon vehicle design. For this reason, it is difficult to optimize every vehicle parameter for maximum fuel efficiency.

In this study, a simulation is developed by using basic vehicle …