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Control and State-Estimation of Jump Stochastic Systems by Learning Recurrent Spatiotemporal Patterns


Han, SooJean (2023) Control and State-Estimation of Jump Stochastic Systems by Learning Recurrent Spatiotemporal Patterns. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/gyae-jv94.


This thesis establishes control and estimation architectures that combine both model-based and model-free methods by theoretically characterizing several types of jump stochastic systems (JSSs), i.e., systems with random and repetitive jump phenomena. By expanding the capabilities of model-based stochastic control and estimation, there is potential for artificial intelligence to be implemented as a supplement to theory-influenced design instead of being used end-to-end. We begin by deriving sufficient conditions for stochastic incremental stability for nonlinear systems perturbed by two types of non-Gaussian noise: 1) shot noise processes represented as compound Poisson processes, and 2) finite-measure Lévy processes constructed as affine combinations of Gaussian white and Poisson shot noise processes. We then present a controller architecture based on a concept we call pattern-learning for prediction (PLP) for discrete-time/discrete-event systems, in which we can take advantage of the fact that the underlying jump process is a sequence of random variables that occurs as repeated patterns of interest. Finally, we demonstrate control and estimation for JSSs in three real-world applications. First, we consider the control of networks with dynamic topology (e.g., power grid with fault-tolerance to downed lines), for which PLP is integrated with variations of the novel system-level synthesis framework for disturbance-rejection. Second, we perform congestion control of vehicle traffic flow over metropolitan intersection networks, for which PLP is extended to pattern-learning with memory and prediction (PLMP) via the inclusion of episodic control, designed to reduce memory consumption by exploiting structural symmetries and temporal repetition in the network. Third, we perform estimation and forecasting (the dual problem to control) for epidemic spread throughout a population network under jump phenomena such as superspreader effects and the emergence of variant viruses. Our results indicate that learning patterns in the jump process makes controller/observer design efficient in data-consumption and computation time, which suggests that it can potentially be used for other JSSs in the real world.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:stochastic control theory; jump processes; uncertain systems; data-driven analysis; networked systems
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Control and Dynamical Systems
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Doyle, John Comstock (co-advisor)
  • Chung, Soon-Jo (co-advisor)
Thesis Committee:
  • Murray, Richard M. (chair)
  • Doyle, John Comstock
  • Chung, Soon-Jo
  • Wierman, Adam C.
Defense Date:12 January 2023
Non-Caltech Author Email:han.soojean (AT)
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
Aerospace CorporationUNSPECIFIED
Record Number:CaltechTHESIS:01302023-023806052
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Ch. 2 adapted for Ch. 3 and Ch. 4 adapted for Ch. 4 adapted for Ch. 5
Han, SooJean0000-0003-1195-6465
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15094
Deposited By: SooJean Han
Deposited On:14 Feb 2023 20:37
Last Modified:03 Jan 2024 01:03

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