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Full-Text Articles in Controls and Control Theory
On The Synthesis Of Optimal Control Laws, Meir Pachter, Isaac E. Weintraub
On The Synthesis Of Optimal Control Laws, Meir Pachter, Isaac E. Weintraub
Faculty Publications
In this paper we advocate for Isaacs' method for the solution of differential games to be applied to the solution of optimal control problems. To make the argument, the vehicle employed is Pontryagin's canonical optimal control example, which entails a double integrator plant. However, rather than controlling the state to the origin, we correctly require the end state to reach a terminal set that contains the origin in its interior. Indeed, in practice, it is required to control to a prescribed tolerance rather than reach a desired end state; achieving tight tolerances is expensive, and from a theoretical point of …
Torque Vectoring To Maximize Straight-Line Efficiency In An All-Electric Vehicle With Independent Rear Motor Control, William Blake Brown
Torque Vectoring To Maximize Straight-Line Efficiency In An All-Electric Vehicle With Independent Rear Motor Control, William Blake Brown
Theses and Dissertations
BEVs are a critical pathway towards achieving energy independence and meeting greenhouse and pollutant gas reduction goals in the current and future transportation sector [1]. Automotive manufacturers are increasingly investing in the refinement of electric vehicles as they are becoming an increasingly popular response to the global need for reduced transportation emissions. Therefore, there is a desire to extract the most fuel economy from a vehicle as possible. Some areas that manufacturers spend much effort on include minimizing the vehicle’s mass, body drag coefficient, and drag within the powertrain. When these values are defined or unchangeable, interest is driven to …
A Study Of Deep Reinforcement Learning In Autonomous Racing Using Deepracer Car, Mukesh Ghimire
A Study Of Deep Reinforcement Learning In Autonomous Racing Using Deepracer Car, Mukesh Ghimire
Honors Theses
Reinforcement learning is thought to be a promising branch of machine learning that has the potential to help us develop an Artificial General Intelligence (AGI) machine. Among the machine learning algorithms, primarily, supervised, semi supervised, unsupervised and reinforcement learning, reinforcement learning is different in a sense that it explores the environment without prior knowledge, and determines the optimal action. This study attempts to understand the concept behind reinforcement learning, the mathematics behind it and see it in action by deploying the trained model in Amazon's DeepRacer car. DeepRacer, a 1/18th scaled autonomous car, is the agent which is trained …
Electromagnetic Formation Control Using Frequency Multiplexing, Zahra Abbasi
Electromagnetic Formation Control Using Frequency Multiplexing, Zahra Abbasi
Theses and Dissertations--Mechanical Engineering
This dissertation addresses control of relative positions and orientations of formation flying satellites using magnetic interactions. Electromagnetic formation flight (EMFF) is implemented, in which each satellite is equipped with a set of electromagnetic coils to generate an electromagnetic field. Traditional EMFF technique applies DC magnetic fields which lead to a nonlinear and highly coupled formation dynamics that allow for only position or orientation control of the satellites. We present a new frequency multiplexing method, which is a technique that uses multi-frequency sinusoidal controls, to approximately decouple the formation dynamics and to provide enough controls for both position and orientation control. …