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  <title><![CDATA[PhD Defense by Kaivalya Bakshi]]></title>
  <body><![CDATA[<p><strong>Ph.D. Thesis Announcement</strong></p>

<p>&nbsp;</p>

<p>By</p>

<p>&nbsp;</p>

<p>Kaivalya Bakshi</p>

<p>(Advisor: Prof. Evangelos Theodorou)</p>

<p>2:00 PM, Thursday 12 Nov 2018</p>

<p><em>Klaus Building, Conference room 2108</em></p>

<p>&nbsp;</p>

<p><strong>LARGE SCALE STOCHASTIC CONTROL: ALGORITHMS, OPTIMALITY</strong></p>

<p><strong>AND STABILITY</strong></p>

<p><strong>Summary: </strong></p>

<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Optimal control of large-scale multi-agent networked systems which describe social</p>

<p>networks, macro-economies, traffic and robot swarms is a topic of interest in engineer-</p>

<p>ing, biophysics and economics. A central issue is constructing scalable control-theoretic</p>

<p>frameworks when the number of agents is infinite.</p>

<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; In this work, we exploit PDE representations of the optimality laws in order to provide</p>

<p>a tractable approach to ensemble (open loop) and closed loop control of such systems. A</p>

<p>centralized open loop optimal control problem of an ensemble of agents driven by jump</p>

<p>noise is solved by a sampling algorithm based on the infinite dimensional minimum prin-</p>

<p>ciple to solve it. The relationship between the infinite dimensional minimum principle and</p>

<p>dynamic programming principles is established for this problem.</p>

<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Mean field game (MFG) models expressed as PDE systems are used to describe emer-</p>

<p>gent phenomenon in decentralized feedback optimal control models of a continuum of</p>

<p>interacting agents with stochastic dynamics. However, stability analysis of MFG models</p>

<p>remains a challenging problem, since they exhibit non-unique solutions in the absence of a</p>

<p>monotonicity assumption on the cost function. This thesis addresses the key issue of sta-</p>

<p>bility and control design in MFGs. Specifically, we present detailed results on a models for</p>

<p>flocking and population evolution.</p>

<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; An interesting connection between MFG models and the imaginary-time Schr&ouml;dinger</p>

<p>equation is used to obtain explicit stability constraints on the control design in the case</p>

<p>of non-interacting agents. Compared to prior works on this topic which apply only to</p>

<p>agents obeying very simple integrator dynamics, we treat nonlinear agent dynamics and</p>

<p>also provide analytical design constraints.</p>

<p><strong>Committee Members:</strong></p>

<p>Prof. Evangelos Theodorou (Advisor)</p>

<p>Dr. Piyush Grover (Principal Research Scientist, MERL)</p>

<p>Prof. Eric Feron (AE)</p>

<p>Prof. Yongxin Chen (AE)</p>

<p>Prof. Ionel Popescu (MATH)</p>

<p>Prof. Paul Bogdan (EE, USC Viterbi)</p>

<p>&nbsp;</p>
]]></body>
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