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2. Lyapunov time - Wikipedia

   en.wikipedia.org/wiki/Lyapunov_time

   The Lyapunov time mirrors the limits of the predictability of the system. By convention, it is defined as the time for the distance between nearby trajectories of the system to increase by a factor of e. However, measures in terms of 2-foldings and 10-foldings are sometimes found, since they correspond to the loss of one bit of information or ...

3. Lyapunov exponent - Wikipedia

   en.wikipedia.org/wiki/Lyapunov_exponent

   In 1930 O. Perron constructed an example of a second-order system, where the first approximation has negative Lyapunov exponents along a zero solution of the original system but, at the same time, this zero solution of the original nonlinear system is Lyapunov unstable. Furthermore, in a certain neighborhood of this zero solution almost all ...

4. Controllability Gramian - Wikipedia

   en.wikipedia.org/wiki/Controllability_Gramian

   Linear Time Invariant (LTI) Systems are those systems in which the parameters , , and are invariant with respect to time. One can observe if the LTI system is or is not controllable simply by looking at the pair ( A , B ) {\displaystyle ({\boldsymbol {A}},{\boldsymbol {B}})} .

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6. Lyapunov equation - Wikipedia

   en.wikipedia.org/wiki/Lyapunov_equation

   In particular, the discrete-time Lyapunov equation (also known as Stein equation) for is A X A H − X + Q = 0 {\displaystyle AXA^{H}-X+Q=0} where Q {\displaystyle Q} is a Hermitian matrix and A H {\displaystyle A^{H}} is the conjugate transpose of A {\displaystyle A} , while the continuous-time Lyapunov equation is

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8. Control-Lyapunov function - Wikipedia

   en.wikipedia.org/wiki/Control-Lyapunov_function

   The ordinary Lyapunov function is used to test whether a dynamical system is (Lyapunov) stable or (more restrictively) asymptotically stable. Lyapunov stability means that if the system starts in a state x ≠ 0 {\displaystyle x\neq 0} in some domain D , then the state will remain in D for all time.

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