TheoremComplete

Lyapunov's Direct Method

Lyapunov's direct (or second) method determines stability without solving the differential equation, by finding an appropriate energy-like function.


Statement

Theorem7.7Lyapunov stability theorem (complete)

Let x0\mathbf{x}_0 be an equilibrium of x=F(x)\mathbf{x}' = \mathbf{F}(\mathbf{x}) and let V:DRV: D \to \mathbb{R} be a C1C^1 function on a neighborhood DD of x0\mathbf{x}_0 with V(x0)=0V(\mathbf{x}_0) = 0.

  1. If VV is positive definite and V˙0\dot{V} \leq 0 (negative semi-definite), then x0\mathbf{x}_0 is stable.
  2. If VV is positive definite and V˙\dot{V} is negative definite, then x0\mathbf{x}_0 is asymptotically stable.
  3. If VV is positive definite, V˙\dot{V} is negative semi-definite, and the only solution of x=F(x)\mathbf{x}' = \mathbf{F}(\mathbf{x}) that stays in {V˙=0}\{\dot{V} = 0\} is x(t)x0\mathbf{x}(t) \equiv \mathbf{x}_0, then x0\mathbf{x}_0 is asymptotically stable (LaSalle).

Proof of Part 1 (Stability)

Proof

Given ε>0\varepsilon > 0, let Bε={xx0<ε}DB_\varepsilon = \{\|\mathbf{x} - \mathbf{x}_0\| < \varepsilon\} \subset D. Since VV is positive definite and continuous, m=minxx0=εV(x)>0m = \min_{\|\mathbf{x}-\mathbf{x}_0\|=\varepsilon} V(\mathbf{x}) > 0.

Since VV is continuous and V(x0)=0V(\mathbf{x}_0) = 0, there exists δ>0\delta > 0 such that V(x)<mV(\mathbf{x}) < m for xx0<δ\|\mathbf{x}-\mathbf{x}_0\| < \delta.

For x(0)x0<δ\|\mathbf{x}(0) - \mathbf{x}_0\| < \delta: V(x(0))<mV(\mathbf{x}(0)) < m. Since V˙0\dot{V} \leq 0, V(x(t))V(x(0))<mV(\mathbf{x}(t)) \leq V(\mathbf{x}(0)) < m for all t0t \geq 0.

If x(t)\mathbf{x}(t) ever reached x(t)x0=ε\|\mathbf{x}(t) - \mathbf{x}_0\| = \varepsilon, then V(x(t))mV(\mathbf{x}(t)) \geq m, contradicting V(x(t))<mV(\mathbf{x}(t)) < m. Therefore x(t)x0<ε\|\mathbf{x}(t) - \mathbf{x}_0\| < \varepsilon for all t0t \geq 0. \blacksquare


Instability Theorem

Theorem7.8Chetaev's instability theorem

Let V:DRV: D \to \mathbb{R} be C1C^1 with V(x0)=0V(\mathbf{x}_0) = 0. If in every neighborhood of x0\mathbf{x}_0 there exists a point where V>0V > 0, and V˙>0\dot{V} > 0 on the set {V>0}D\{V > 0\} \cap D, then x0\mathbf{x}_0 is unstable.

ExampleInstability via Chetaev

For x=y+x(x2+y2)x' = y + x(x^2+y^2), y=x+y(x2+y2)y' = -x + y(x^2+y^2): try V=x2+y2V = x^2 + y^2.

V˙=2x(y+x(x2+y2))+2y(x+y(x2+y2))=2(x2+y2)2>0\dot{V} = 2x(y+x(x^2+y^2)) + 2y(-x+y(x^2+y^2)) = 2(x^2+y^2)^2 > 0 for (x,y)(0,0)(x,y) \neq (0,0).

Since V>0V > 0 near the origin and V˙>0\dot{V} > 0, the origin is unstable.

RemarkComparison with linearization

Lyapunov's method and linearization are complementary:

  • Linearization is easy when eigenvalues have nonzero real parts.
  • Lyapunov functions handle non-hyperbolic cases and provide stability regions.
  • For nonlinear systems, Lyapunov functions can prove global stability, which linearization cannot.