Research Statement of Kwang-Ki K. Kim [PDF]
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Robust Control Theory In robust control framework, system properties such as stability and performances need to be guaranteed for any possible realization of uncertainties in a set-valued system model. |
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Analysis and Synthetic Design of Large-scale Interconnected/Networked Systems For analysis and controller design of large-scale interconnected/networked systems, one needs mathematical tools to exploit the interconnection/network structures. |
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Semidefinite Programming Relaxation for Nonconvex Programs Nonconvexity generally results in NP-hard problems, which makes the associated large-scale problems intractable. |
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Robust Optimization The presence of uncertain data in optimization can significantly degrade performance of an nominal optimal solution. To guarantee robustness of an optimal solution, one needs to take the uncertainty into account in the stage of problem formulation. |
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Uncertainty Quantification of Dynamical Systems: Data-driven and Model-based Robust Analysis and Design Analyzing and quantifying uncertainty propagation through a dynamical system model have been important research. |
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Statistical Decision Theory: (Approximate) Stochastic Programming The decision principles of stochastic programming have a statistical background. The science of decision making under stochastic uncertainty has been extensively studied by many different research communities. |
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System Identification In systems and control theory, working with a system model is indispensable and obtaining a proper system model for the true process play a crucial role for analysis and synthesis problems. |
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Robust and Adaptive Optimal Experiment Design for System Identification For system identification or parameter estimation, it is important to analyze how informative the measurement/observable data are and generate sufficiently informative sets of data for rapid and accurate convergence of identification or estimation procedure. |
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Fault Detection and Diagnosis Algorithms, and Robust Fault Tolerant Control During operation of a process, some of the unit might be malfunctioning. When a fault/failure of a system component occurs, one needs tools to designate its location and remedy it for the system to recover. |
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Robust Approximate Dynamic Programming In principle, the optimal control problem is solved once the optimal cost-to-go function is known (or estimated) and the DP formulation of dynamic optimization for solving multi-stage problems provides a convenient way to solve via an equivalent single-stage optimization. |
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Deterministic and Stochastic Reachability and Viability Analysis Characterizing the reachability sets of deterministic and stochastic dynamical systems and indispensable in systems and control science and engineering. |
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Stochastic Model Predictive Control Model predictive control has been powerful tools in many application problems. |
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Validation & Verification of Control Algorithms: Finding Hidden Falsification Complex mathematics might mask falisfication of a control method. A natural way to verify and validate a given control method is to find a counter example. |
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Control of Chemical Reaction Process Chemical reactions can be represented as kinetic models and/or master equations. |
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Systems Biology Understanding how biological systems maintain proper functions is very important. |
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Distributed Control of Power Systems |
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Quality by Design: Less Conservative Search for Design Space |
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Financial Engineering: Pricing Mechanism |
