Abstract
Vehicles of all sorts are important tools of human activities. Modern autonomous vehicles are built to perform complicated tasks that are beyond their basic function as transportation tools. Examples include robots that autonomously explore the unknown environments, and self-driving vehicles that safely navigate on the highways. Because their working environments are highly dynamic or unknown, autonomous vehicles need to make decisions and react to their changing environments at every time instance. This decision-making problem can be solved using constrained optimal control methods. However, solving such problems in real time is prohibitive on most vehicles because current methods take a large amount of computational resources and most vehicles lack that level of computational power. In this dissertation, a new adaptive dynamic programming method with reduced computation requirement is developed to solve this type of problems. Based on a bioinspired search strategy and the knowledge of vehicle dynamics, the new method can help vehicles make decisions in real time with a fraction of the computational resources required by other typical constrained optimal control methods. An unmanned aerial vehicle flight control problem and a ground vehicle obstacle avoidance problem are used to test the performance of the new method in simulation. A scouting robot has successfully adopted this new method for its navigation in a local farm.
Notes
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Graduation Date
2021
Semester
Summer
Advisor
Xu, Yunjun
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Mechanical and Aerospace Engineering
Degree Program
Mechanical Engineering
Format
application/pdf
Identifier
CFE0009116; DP0026449
URL
https://purls.library.ucf.edu/go/DP0026449
Language
English
Release Date
February 2027
Length of Campus-only Access
5 years
Access Status
Doctoral Dissertation (Campus-only Access)
STARS Citation
Li, Qiang, "Adaptive Dynamic Programming in a Subspace for Some Discrete-Time Vehicle Systems" (2021). Electronic Theses and Dissertations, 2020-2023. 1145.
https://stars.library.ucf.edu/etd2020/1145
Restricted to the UCF community until February 2027; it will then be open access.