ORCID Identifier(s)

0000-0002-4332-3462

Graduation Semester and Year

2021

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering

Department

Electrical Engineering

First Advisor

Frank Lewis

Abstract

This dissertation studies the problem of data-driven optimal decision making. The 4main contributions of this work are listed here. First, we develop a model-based and data-driven techniques for learning the cost of an Ex-pert agent. This ties fields of Inverse Optimal Control and Inverse Reinforcement Learning and represents a first data-driven algorithm of this kind in the control community. Next, we have developed optimally adaptive dynamic control allocation mechanism that optimally re-configures redundant actuators in a model-free fashion, that is, based on collected data. This work pushed the multiple frontiers of control allocation research, since state-of-the-art control allocation was Next, we have introduced an uncertainty aware trajectory optimization technique that uses the information about the model uncertainty to inform the generation of local feedback policy which makes the open loop solution more reliable and robust. Finally, a cooperative protocol for distributed formation control has been developed and tested on the real system in the lab. This was among the first real world examples of multi-agent distributed formation control.

Keywords

Optimal Control, Control allocation, Dynamic programming, Reinforcement learning, Rational decision making, Data-driven control, Inverse reinforcement learning, Inverse optimal control, Machine learning, Control, Robotics, Swarm, Distributed systems

Disciplines

Electrical and Computer Engineering | Engineering

Comments

Degree granted by The University of Texas at Arlington

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