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Introduction and Literature Survey of Statistical Control: Going Beyond the Mean

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Advances in Statistical Control, Algebraic Systems Theory, and Dynamic Systems Characteristics

Part of the book series: Systems & Control: Foundations & Applications ((SCFA))

Summary

In traditional optimal control, the system is modeled as a stochastic differential equation and an optimal controller is determined such that the expected value of a cost function is minimized. An example is the well-known linear-quadratic-Gaussian problem that has been studied extensively since the 1960s. The mean or the first cumulant is a useful performance metric, however, the mean is only one of the cumulants that describe the distribution of a random variable. It is possible for the operator to optimize the whole distribution of the cost function instead of just the mean. In fact, a denumerable sum of all the cost cumulants has been optimized in risk-sensitive control. The key idea behind statistical control is to optimize other statistical quantities such as the variance, skewness, and kurtosis of the cost function. This leads to the optimal performance shaping concept. Furthermore, we use this statistical concept to generalize \(H_\infty\) and multiple player game theory. In both traditional \(H_\infty\) theory and game theory, the mean of the cost function was the object of optimization, and we can extend this to the optimization of any cumulants if we utilize the statistical control concept. In this chapter, we formulate and provide a literature survey of statistical control. We also review minimal cost variance (second cumulant) control, \(k\)th cost cumulant control, and multiobjective cumulant games. Furthermore, risk-sensitive control is presented as a special case of statistical control. When we view the cost function as a random variable, and optimize any of the cost cumulants, linear-quadratic-Gaussian, minimal cost variance, risk-sensitive, game theoretic, and \(H_\infty\) control all fall under the umbrella of statistical control. Finally, we interpret the cost functions via utility functions.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19122, USA

    Chang-Hee Won

  2. Department of Engineering, University of Southern Indiana, Evansville, IN 47712, USA

    Ronald W. Diersing

  3. Department of Electrical Engineering, The Ohio State University, Columbus, OH 43210, USA

    Stephen Yurkovich

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  1. Chang-Hee Won
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  2. Ronald W. Diersing
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  3. Stephen Yurkovich
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Correspondence to Chang-Hee Won .

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Editors and Affiliations

  1. Dept. Electrical &, Computer Engineering, Temple University , North 12th Street 1947, Philadelphia, 19122, U.S.A.

    Chang-Hee Won

  2. College of Engineering, Boise State University, University Drive 1910, Boise, 83725-2100, U.S.A.

    Cheryl B. Schrader

  3. Department of Electrical Engineering, University of Notre Dame, Fitzpatrick Hall 275, Notre Dame, 46556-5637, U.S.A.

    Anthony N. Michel

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Won, CH., Diersing, R.W., Yurkovich, S. (2008). Introduction and Literature Survey of Statistical Control: Going Beyond the Mean. In: Won, CH., Schrader, C., Michel, A. (eds) Advances in Statistical Control, Algebraic Systems Theory, and Dynamic Systems Characteristics. Systems & Control: Foundations & Applications. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-4795-7_1

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