Title:
Decision-Making Architectures for Control of Uncertain Systems
Decision-Making Architectures for Control of Uncertain Systems
Author(s)
Gandhi, Manan
Advisor(s)
Theodorou, Evangelos
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Abstract
From aggressive driving, to drone racing, to cardiovascular control, each of these problems involves uncertain dynamics and a requirement to make decisions. There exist both classical and modern techniques for control of dynamical systems which can be used to favor safety, exploration, control performance, or other criteria. Information Theoretic Model Predictive Control (IF-MPC) has been utilized to great effect for autonomous racing by optimizing the system free energy through forward sampling. This dissertation explores the ideas of incorporating safety directly into IF-MPC for uncertain systems. The underpinnings of Model Predictive Path Integral control are presented with its connections to both Information Theory and Bayesian inference. Current research on model learning for biological systems are presented with aim to use more advanced control techniques. The core of the thesis is the development on a framework to perform safe model predictive control of stochastic dynamical systems while taking advantage of traditional control techniques to make decisions during operation. In the development of this novel framework, the foundation of modern, safe control are presented, along with state-of-the-art developments to enable safe control with fewer theoretical and computational restrictions. This work builds upon the idea of augmented importance sampling in IF-MPC to perform tasks under safety constraints for a general system. The control architecture presented here can handle both soft and hard constraints on the state of a dynamical system, as well as balance task completion with the notion of safety.
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Date Issued
2023-04-27
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Text
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Dissertation