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

  • Chang-Hee Won
  • Ronald W. Diersing
  • Stephen Yurkovich
Part of the Systems & Control: Foundations & Applications book series (SCFA)


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.


Cost Function Utility Function Infinite Horizon Cost Cumulants Quadratic Cost Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Birkhäuser Boston, a part of Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Chang-Hee Won
    • 1
  • Ronald W. Diersing
    • 2
  • Stephen Yurkovich
    • 3
  1. 1.Department of Electrical and Computer EngineeringTemple UniversityPhiladelphiaUSA
  2. 2.Department of EngineeringUniversity of Southern IndianaEvansvilleUSA
  3. 3.Department of Electrical EngineeringThe Ohio State UniversityColumbusUSA

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