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  2. Lyapunov function - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_function

    A Lyapunov function for an autonomous dynamical system {: ˙ = ()with an equilibrium point at = is a scalar function: that is continuous, has continuous first derivatives, is strictly positive for , and for which the time derivative ˙ = is non positive (these conditions are required on some region containing the origin).

  3. Lyapunov optimization - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_optimization

    Lyapunov functions are used extensively in control theory to ensure different forms of system stability. The state of a system at a particular time is often described by a multi-dimensional vector. A Lyapunov function is a nonnegative scalar measure of this multi-dimensional state.

  4. Lyapunov equation - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_equation

    The Lyapunov equation, named after the Russian mathematician Aleksandr Lyapunov, is a matrix equation used in the stability analysis of linear dynamical systems. [ 1 ] [ 2 ] In particular, the discrete-time Lyapunov equation (also known as Stein equation ) for X {\displaystyle X} is

  5. Lyapunov stability - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_stability

    Lyapunov's realization was that stability can be proven without requiring knowledge of the true physical energy, provided a Lyapunov function can be found to satisfy the above constraints. Definition for discrete-time systems

  6. 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.

  7. Input-to-state stability - Wikipedia

    en.wikipedia.org/wiki/Input-to-state_stability

    It can be easily proved, [13] that if is an iISS-Lyapunov function with , then is actually an ISS-Lyapunov function for a system . This shows in particular, that every ISS system is integral ISS. The converse implication is not true, as the following example shows.

  8. Lyapunov exponent - Wikipedia

    en.wikipedia.org/wiki/Lyapunov_exponent

    Lyapunov proved that if the system of the first approximation is regular (e.g., all systems with constant and periodic coefficients are regular) and its largest Lyapunov exponent is negative, then the solution of the original system is asymptotically Lyapunov stable. Later, it was stated by O. Perron that the requirement of regularity of the ...

  9. Controllability Gramian - Wikipedia

    en.wikipedia.org/wiki/Controllability_Gramian

    If, in addition, all eigenvalues of have negative real parts (is stable), and the unique solution of the Lyapunov equation + = is positive definite, the system is controllable. The solution is called the Controllability Gramian and can be expressed as W c = ∫ 0 ∞ e A τ B B T e A T τ d τ {\displaystyle {\boldsymbol {W_{c}}}=\int _{0 ...