Controls and Control Theory Commons^™

Design, Modeling And Control Of A Two-Wheel Balancing Robot Driven By Bldc Motors, Charles T. Refvem

Master's Theses

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. …

An Application Of Sliding Mode Control To Model-Based Reinforcement Learning, Aaron Thomas Parisi

Master's Theses

The state-of-art model-free reinforcement learning algorithms can generate admissible controls for complicated systems with no prior knowledge of the system dynamics, so long as sufficient (oftentimes millions) of samples are available from the environ- ment. On the other hand, model-based reinforcement learning approaches seek to leverage known optimal or robust control to reinforcement learning tasks by mod- elling the system dynamics and applying well established control algorithms to the system model. Sliding-mode controllers are robust to system disturbance and modelling errors, and have been widely used for high-order nonlinear system control. This thesis studies the application of sliding mode control …

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 …

Weight Controlled Electric Skateboard, Zachary Barram, Carson Bertozzi, Vishnu Dodballapur

Computer Engineering

Technology and the way that humans interact is becoming more vital and omnipresent with every passing day. However, human interface device designers suffer from the increasingly popular “designed for me or people like me” syndrome. This design philosophy inherently limits accessibility and usability of technology to those like the designer. This places severe limits of usability to those who are not fully able as well as leaves non-traditional human interface devices unexplored. This project set out to explore a previously uncharted human interface device, on an electric skateboard, and compare it send user experience with industry leading human interface devices.

Flight Director Embedded System And Mobile Ios Application, Anthony Epshteyn

Computer Engineering

For my senior project, I was asked to assist an Aerospace Engineering professor with the design of his new glider. He needed to create a flight director-type instrument so that his pilot could get the aircraft’s positional data during flight. This positional data came from a powerful sensor mounted on the fuselage. To interface with the sensor, I created an embedded system comprised of a ESP32 micro-controller communicating with the sensor via UART/RS-232. The micro-controller was mounted on a breadboard and connected to the sensor via jumper wires. The ESP32 featured a Bluetooth chip that allowed for communication using the …

Underwater Remotely Operated Vehicle Controller With Pid Stability Regulation, Christian E. Aguirre

Electrical Engineering

The earth’s oceans and rivers remain widely unexplored. Hobbyists and companies across the globe invest money and resources into remotely operated vehicles (ROV) to further expand underwater knowledge. Each ROV includes a controller that operates motors and monitors other crucial system vitals. The ROV Controller project makes the process of designing an ROV simpler and more affordable by providing a multi-purpose programmable controller.

The ROV controller features programmable digital inputs/outputs and analog inputs. The controller processes control signals from analog joysticks, digital signals from a gyroscope and utilizes a MUX to expand the analog input capabilities of the Arduino. The …

Viewpoint Optimization For Autonomous Strawberry Harvesting With Deep Reinforcement Learning, Jonathon J. Sather

Master's Theses

Autonomous harvesting may provide a viable solution to mounting labor pressures in the United States' strawberry industry. However, due to bottlenecks in machine perception and economic viability, a profitable and commercially adopted strawberry harvesting system remains elusive. In this research, we explore the feasibility of using deep reinforcement learning to overcome these bottlenecks and develop a practical algorithm to address the sub-objective of viewpoint optimization, or the development of a control policy to direct a camera to favorable vantage points for autonomous harvesting. We evaluate the algorithm's performance in a custom, open-source simulated environment and observe affirmative results. Our trained …