Term of Award
Master of Science, Electrical Engineering
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Thesis (open access)
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This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Electrical and Computer Engineering
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Reinforcement learning (RL) has attracted large attention over the past few years. Recently, we developed a data-driven algorithm to solve predictive cruise control (PCC) and games output regulation problems. This work integrates our recent contributions to the application of RL in game theory, output regulation problems, robust control, small-gain theory and PCC. The algorithm was developed for $H_\infty$ adaptive optimal output regulation of uncertain linear systems, and uncertain partially linear systems to reject disturbance and also force the output of the systems to asymptotically track a reference. In the PCC problem, we determined the reference velocity for each autonomous vehicle in the platoon using the traffic information broadcasted from the lights to reduce the vehicles' trip time. Then we employed the algorithm to design an approximate optimal controller for the vehicles. This controller is able to regulate the headway, velocity and acceleration of each vehicle to the desired values. Simulation results validate the effectiveness of the algorithms.
Odekunle, Adedapo O., "Reinforcement Learning, Intelligent Control and their Applications in Connected and Autonomous Vehicles" (2019). Electronic Theses and Dissertations. 1878.
Research Data and Supplementary Material