Examine the stability of the rest point (0,0) of the system…
Examine the stability of the rest point $(0,0)$ of the system $$\dot{x}=-\sin x+3y+x^5,\qquad\dot{y}=\tfrac{1}{4}x-2y-\tfrac{1}{6}y^3,$$ by the method of first approximation (linearisation).
Explanation of Method
The rest point's stability is determined by Lyapunov's First Method (the linearisation / first-approximation theorem). The nonlinear system is split into its linear part — the Jacobian matrix at the rest point — plus a remainder that is verified to be o(r) as the distance r to the equilibrium tends to zero. Provided no eigenvalue of the Jacobian has zero real part, those eigenvalues alone determine the stability of the full nonlinear system, and by the Hartman–Grobman theorem they also determine its qualitative type (node, saddle, or focus).
Step 1 · Verification that $(0,0)$ is a rest point
At $(0,0)$: $-\sin0+3\cdot0+0=0$ and $\tfrac14\cdot0-2\cdot0-0=0$.
Step 2 · Separation of linear and nonlinear parts
Using $-\sin x=-x+\tfrac{x^3}{6}+O(x^5)$: $$\dot x=-x+3y+\underbrace{\tfrac{x^3}{6}+\tfrac{119x^5}{120}+\cdots}_{=:f_2}, \qquad \dot y=\tfrac14x-2y+\underbrace{\left(-\tfrac16y^3\right)}_{=:g_2}.$$ Both remainders satisfy $f_2,g_2=O(r^3)=o(r)$ as $r\to0$.
Step 3 · Jacobian matrix at $(0,0)$
$$A=\begin{pmatrix}-1&3\\\tfrac14&-2\end{pmatrix}.$$
Step 4 · Characteristic equation
$$\det(A-\lambda I)=0\implies4\lambda^2+12\lambda+5=0 \implies\lambda=\frac{-12\pm8}{8}.$$ Thus $\lambda_1=-\tfrac12$, $\lambda_2=-\tfrac52$.
Step 5 · Classification of eigenvalues
Both real, distinct, negative: $\lambda_2=-\tfrac52<\lambda_1=-\tfrac12<0$.
Step 6 · Verification of the $o(r)$ condition
For $r\leq1$: $|f_2|\leq\tfrac{139}{120}r^3$, $|g_2|\leq\tfrac16r^3$, so $\|F_2\|/r\leq Cr^2\to0$.
Step 7 · Application of the linearisation theorem
Since both eigenvalues have strictly negative real part and the nonlinear terms are $o(r)$, Lyapunov's First Method gives asymptotic stability of the nonlinear rest point.
Step 8 · Routh--Hurwitz verification
For $\lambda^2+3\lambda+\tfrac54$: $a_1=3>0$, $a_2=\tfrac54>0$ — both satisfied.
Step 9 · Eigenvectors and nodal structure
For $\lambda_1=-\tfrac12$: eigenvector $\mathbf v^{(1)}=(6,1)^\top$ (slower axis, tangent to $y=\tfrac16x$). For $\lambda_2=-\tfrac52$: eigenvector $\mathbf v^{(2)}=(-2,1)^\top$ (faster axis, tangent to $y=-\tfrac12x$).
Step 10 · General solution of the linearised system
$$\xi(t)=6C_1e^{-t/2}-2C_2e^{-5t/2},\qquad \eta(t)=C_1e^{-t/2}+C_2e^{-5t/2}.$$ Both terms decay; the dominant (slower) rate $e^{-t/2}$ governs the approach to the origin along $y=\tfrac16x$ — a stable node.
Step 11 · Behaviour of the nonlinear system
By the Hartman--Grobman theorem (real, negative, distinct eigenvalues, none zero), the nonlinear system is topologically conjugate to its linearisation near the origin: a genuine stable node.
Step 12 · Stability analysis
Since $\operatorname{Re}(\lambda_{1,2})<0$, the rest point is both Lyapunov stable and asymptotically stable.
Step 13 · Conclusion
$$\boxed{ (0,0)\text{ is an \textbf{asymptotically stable node} of the nonlinear system.} }$$ The eigenvalues of the Jacobian at the origin are $\lambda_1=-\tfrac12<0$ and $\lambda_2=-\tfrac52<0$, both real and negative. By Lyapunov's First Method, the nonlinear terms $f_2=O(r^3)$ and $g_2=O(r^3)$ do not affect the conclusion, and the rest point is asymptotically stable, with trajectories approaching it tangentially to $y=\tfrac16x$.
Summary Table
| Quantity | Value |
|---|---|
| Eigenvalues | — |
| Classification | asymptotically stable node |
Additional Notes
- The Hartman–Grobman theorem guarantees that, because no eigenvalue of the Jacobian is purely imaginary, the nonlinear phase portrait near the rest point is topologically equivalent to that of its linearisation — so the type found for the linear system (node, saddle, or focus) carries over unchanged to the full nonlinear system.
Result
The final classification is: