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  2. Controllability - Wikipedia

    en.wikipedia.org/wiki/Controllability

    A controllable system is not necessarily output controllable. For example, if matrix D = 0 and matrix C does not have full row rank, then some positions of the output are masked by the limiting structure of the output matrix, and therefore unachievable. Moreover, even though the system can be moved to any state in finite time, there may be some ...

  3. Controlling for a variable - Wikipedia

    en.wikipedia.org/wiki/Controlling_for_a_variable

    Instead, they must control for variables using statistics. Observational studies are used when controlled experiments may be unethical or impractical. For instance, if a researcher wished to study the effect of unemployment ( the independent variable ) on health ( the dependent variable ), it would be considered unethical by institutional ...

  4. Control variable - Wikipedia

    en.wikipedia.org/wiki/Control_variable

    A variable in an experiment which is held constant in order to assess the relationship between multiple variables [a], is a control variable. [2] [3] A control variable is an element that is not changed throughout an experiment because its unchanging state allows better understanding of the relationship between the other variables being tested.

  5. Control theory - Wikipedia

    en.wikipedia.org/wiki/Control_theory

    Controllability is related to the possibility of forcing the system into a particular state by using an appropriate control signal. If a state is not controllable, then no signal will ever be able to control the state. If a state is not controllable, but its dynamics are stable, then the state is termed stabilizable.

  6. Robust parameter design - Wikipedia

    en.wikipedia.org/wiki/Robust_parameter_design

    A robust parameter design, introduced by Genichi Taguchi, is an experimental design used to exploit the interaction between control and uncontrollable noise variables by robustification—finding the settings of the control factors that minimize response variation from uncontrollable factors. [1]

  7. Probabilistic design - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_design

    Typically, the goal of probabilistic design is to identify the design that will exhibit the smallest effects of random variability. Minimizing random variability is essential to probabilistic design because it limits uncontrollable factors, while also providing a much more precise determination of failure probability.

  8. Blocking (statistics) - Wikipedia

    en.wikipedia.org/wiki/Blocking_(statistics)

    A nuisance factor is used as a blocking factor if every level of the primary factor occurs the same number of times with each level of the nuisance factor. [3] The analysis of the experiment will focus on the effect of varying levels of the primary factor within each block of the experiment.

  9. Ackermann's formula - Wikipedia

    en.wikipedia.org/wiki/Ackermann's_Formula

    If the system is controllable, there is always an input u(t) such that any state x 0 can be transferred to any other state x(t). With that in mind, a feedback loop can be added to the system with the control input u(t) = r(t) − kx(t), such that the new dynamics of the system will be