My PhD Work.
I am a Ph.D. in Electrical Engineering with an emphasis on Systems and Controls. During my PhD, I worked in Nanodynamics Lab in Department of Electrical and Computer Engineering at Iowa State University. My advisor is Dr. Murti V. Salapaka (he is presently at University of Minnesota, Minneaplois, MN, new link).
Reserach publications (click on the title to see the abstract if you are using IE):
I. Distributed Controller Design
Recent technology has led to systems where it is critical to identify architectures that distribute the controller effort over sub-controllers to respect the information flow and/or resource constraints. The communication uncertainty between sub-controllers partly governs the optimality of the architecture of the controller. The related synthesis methodology for optimal distributed controller has to address internal stability concerns and has to incorporate the effect of communication uncertainty into the performance metric. In this article, such a methodology is developed to address the concerns of sub-controller communication uncertainty. It is demonstrated that different canonical architectures of a centralized design result in appreciably different performance. Methods to identify architectures of information flow where the optimal performance problem is convex are developed. In addition, synthesis methods to incorporate robustness measures with respect to model uncertainty of the communication channel are obtained for the associated distributed architectures. These methods are further refined for specific structures of information flow. A Matlab based software tool is developed and efficacy of the approach is demonstrated on application problems.
In this paper, we use state space approach to give a sufficient condition for internal stability of the closed loop system when the centralized stabilizing controller is implemented in a distributive manner. Using this condition, we show that the centralized stabilizing controller for a 2-nest system can be split into two sub-controllers without affecting the internal stability. The effect of sub-controller to sub-controller communication noise on the performance is considered along with the constraint on strength of subcontroller to sub-controller communication signal. We take an input-output approach. In a 2-nest case, we obtain a sufficient condition for splitting the stabilizing controller such that the overall performance optimization can be cast as a convex problem in the Youla-Kucera parameter Q. We also present an architecture for distributive implementation of banded structure controllers such that all closed loop maps are affine in Q.
In this paper we consider how to distribute and implement an unstructured or structured overall controller K to various stations (sub-controllers). In doing so In doing so we assume that noise is present in the sub-controller to subcontroller communication and thus, its effect on stability and performance has to be addressed. Using an observer based controller parameterization, we provide suitable stabilizing sub-controller architectures that directly take into account the effect of communication noise on performance. In particular, the overall performance optimization can be cast as a convex problem in the Youla-Kucera parameter Q. Similar results hold for banded controller structures, i.e., when there is also a delay in the subsystem to subsystem communication.
This paper addresses the design of stabilizing controllers for a nested control system where the controller is realized in a distributed manner, considering uncertainty not only in the controller-to-plant and plant-to-controller channels but also in the nest-to-nest, subcontroller-to-subcontroller communication. An input-output approach is taken. Two appropriate controller architectures that stabilize the entire system with the various components subject to uncertainty and noise are addressed. We present controller synthesis procedures to address deterministic uncertainty and stochastic uncertainty, that model packet-loss in the Internet.
II. Distributed Decision and Consensus Control
Distributed averaging is a well studied problem that converges asymptotically; however, existing protocols do not provide a way for each node to distributively detect the occurrence of convergence. A method to distributively detect when the consensus has reached within a given error margin in finite time is presented in this paper. In absence of such a method, all nodes in the network keep running the required computation and communication even if the consensus value are within acceptable tolerance, which is not desirable as in large-scale distributed networks resources like power are limited. Furthermore, this extra communication can cause signal interference with other critical information.
Distributed averaging over a large network is a well studied problem that converges asymptotically; however, existing protocols does not provide a way for each node to distributively detect the occurrence of convergence. In this paper a method is developed to distributively determine when the consensus has reached within a given error margin. In absence of such a method all nodes in the network keep running the required computation and communication even if the consensus value are within acceptable tolerance, which is not preferable as in large-scale distributed networks resources like power are limited. Furthermore, this extra communication can cause signal interference with other critical information. This distributed detection takes finite time and occurs at each node simultaneously.
In this paper, we study phase transition behavior emerging from the interactions among multiple agents in the presence of noise. We propose a simple discrete-time model in which a group of non-mobile agents form either a fixed connected graph or a random graph process, and each agent, taking bipolar value either +1 or −1, updates its value according to its previous value and the noisy measurements of the connected agents’ values. We present proofs for the occurrence of the following phase transition behavior: At a noise level higher than some threshold, the system generates symmetric behavior (vapor or melt of magnetization) or disagreement; whereas at a noise level lower than the threshold, the system exhibits spontaneous symmetry breaking (solid or magnetization) or consensus. The threshold is found analytically. The phase transition holds for any dimension. Finally, we demonstrate the phase transition behavior and all analytic results using simulations. This result may be found useful in the study of the collective behavior of complex systems under communication constraints.
In this paper, we study phase transition behavior emerging from the interactions between multiple agents in the presence of noise. We propose a simple discrete-time model in which a group of non-mobile agents form either a fixed connected graph or a random graph process, and each agent, taking bipolar value either +1 or -1, updates its value according to its previous value and the noisy measurements of the connected agents' values. We present proofs for the occurrence of the following phase transition behavior: At a noise level higher than some threshold, the system generates symmetric behavior; whereas at a noise level lower than the threshold, the system exhibits spontaneous symmetry breaking. We also verify the phase transition using simulations. This result may be found useful in the study of the collective behavior of complex systems under communication constraints.
Distributed controller design and distributed decision making have been hot topics of investigation in the last few years. New technologies have led to systems where it is critical to identify architectures that distribute the controller effort over sub-controllers to respect the information flow and/or resource constraints. The communication uncertainty between sub-controllers partly governs the optimality of the architecture of the controller. The related synthesis methodology for optimal distributed controller has to address internal stability concerns and has to incorporate the effect of communication uncertainty into the performance metric. In the first part of this thesis, a methodology is developed to address the concerns of sub-controller communication uncertainty. It is demonstrated that different canonical architectures of a centralized design result in appreciably different performance. Methods to identify architectures of information flow where the optimal performance problem is convex are developed. In addition, synthesis methods to incorporate robustness measures with respect to model uncertainty of the communication channel are obtained for the associated distributed architectures. These methods are further refined for specific structures of information flow in the system. In the second part of this thesis, issues in distributed decision making in a large network of nodes are discussed, in particular a distributed averaging consensus protocol is considered which converges asymptotically. However, each node individually never comes to know of the occurrence of convergence, and thus it keeps running required computation and communication throughout its life. This is not desired, as in most of the networks the power of each node is a very limited resource. This thesis provides a distributed algorithm through which each node can distributively detect when the convergence has occurred within a given error margin. This distributed detection takes finite time and happens simultaneously.
Ph.D. Final Oral Exam - "Distributed Decision and Control" [PPT]
M.S. Final Oral Exam - "Phase Transitions of Collective Behaviors with Limited Information" [PPT]
Ph.D. Candidacy Exam (Preliminary Exam) - "Distributed Implementation of Controllers in Presence of Subcontroller Uncertainty" [PPT]
Technical Paper Review Exam - "Distributed Coordination of autonomous agents using Nearest Neighbor Rule" [PPT]
Invited Talk at Indian Institute of Technology, Kanpur-India - "Controller Architectures for Distributed Control" [PPT]
Past Work Experience:
Research Assistant, Nanodynamics Systems Lab, Iowa State University, 2002-2007.
Teaching Assistant, Iowa State University, 2001-2002. Worked as teaching assistant for two lab courses titled ‘Introduction to Electronic Circuits’ and ‘Introduction to Electrical Machines’.
Software Engineer, Future Software Limited, Chennai (Madras) India, 2000-2001. Designed and developed code in C/C++ for Network Management functionality of an optical router using a proprietary language.
Technical Skills:
C/C++, Matlab, Pascal, FORTRAN, LaTex, CPLEX, Verilog, Cadence, Synopsis, FEMLAB, ProE, Spice, Mathematica, Linux, Windows.
Biography:
I received Bachelor of Technology degree in Electrical Engineering from Indian Institute of Technology, Kanpur (India) in year 2000. From 2000-2001, I worked as a software engineer in a communication software company called Future Software Ltd. in Chennai (India). I received Master of Science and Doctor of Philosophy degrees in Electrical Engineering (minor in Mathematics) from Iowa State University in year 2007. I was recepient of Research Excellence Award presented by Iowa State Univeristy. My research interests at Iowa State were concerned to distributed control design, studying self organization and phase transition in large scale systems. Presenty, I am working with Garmin International Inc. based at Olathe, KS (USA).
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