Parametric Excitation
Excitation of oscillations through parametric forcing
Parametric Resonance
Destabilization of system response through parametric forcing
Vibrational Stabilization
Parametric stabilization of nominally unstable system behavior
\[I \ddot{\theta} + c \dot{\theta} + k \theta = M(t,\theta) \]
MEMS devices used as capacitive sensors can operate at high frequencies, with high sensitivities and low power requirements.
Can utilize parametric excitation to decouple sensitivity from bandwidth (Q factor)
Capacitive sensing is also sensitive to parasitic signals from the actuating drive signal.
Parametric Excitation
Can achieve very high Q factors not currently attainable with present resonance mode AFM techniques.
The simplest dynamical model of a Stirling engine is the Schmidt model:
\[\ddot{x} + c \dot{x} + \frac{x-y}{1+x-y} = 0\]
Which exhibits parametric resonance for certain periodic displacer motions \(y(t)\).
When linearized about \(x = 0\), with \(y(t) = \epsilon \cos(\omega t)\),
\(\ddot{x} + c \dot{x} + (1+2 \epsilon \cos(\omega t)) x = 0\)
Parametric Resonance
Instability in the system as a result of parametric variation
A rigid pendulum where the pivot point oscillates in the vertical direction.
\[\ddot{\theta} + \left( -\frac{A}{l}\ddot{Y}(t) - \frac{g}{l} \right)\sin(\theta) = 0\]
Vibrational Stabilization
The Kapitza pendulum can be vibrationally stabilized with the right pivot motion \(AY(t)\).
Linear stability of Faraday waves when a container is oscillated vertically [benjamin1954]
Kelvin-Helmholtz instability with time-periodic shear [kelly1965]
Vibrations of columns subjected to oscillating axial loads [iwatsubo1974]
Acoustic instabilities in flames [sammarco1997] and in Rayleigh-Bénard convection [vittori1998]
Stability of Barotropic and Baroclinic shear flows [poulin2003] and [pedlosky2003]
The canonical example of a periodic linear system is Mathieu's Equation,
\[\ddot{x}(t) + (\pm \omega^2 + \epsilon \cos{t}) x(t) = 0\]
\(+\omega^2\): Is a harmonic oscillator with a periodically time-varying spring constant
\(-\omega^2\): Exponentially unstable in the absence of parametric forcing, corresponds to a system like the Kapitza pendulum.
\[ \ddot{x}(t) + 2 \nu \omega \dot{x}(t) + (\pm \omega^2 + \epsilon \cos{t}) x(t) = 0 \]
Can be transformed through a change of coordinates \(\bar{x} = x(t) e^{\nu \omega t}\) to Mathieu's Equation
Mathieu's Equation is a special case of the more general Hill ODE
\[\ddot{x}(t) + f(t) x(t) = 0 \text{, }f(t+T) = f(t)\]
where \(f\) has a single harmonic.
\[x(t) = \Phi(t,t_0) x(t_0)\]
The Hill ODE is formally solved using Floquet theory: Its stability is determined by the eigenvalues of the Monodromy map \(\lambda[\Phi(T,0)]\), where \(\Phi(t,t_0)\) is the state transition matrix.
The Hill ODE is measure preserving, \(\frac{d}{dt} \det \Phi(t,s) = 0\)
\[\ddot{x}(t) + (\pm \omega^2 + \epsilon \cos{t}) x(t) = 0\]
Parametric Amplification or Resonance
\(+\omega^2\): A nominally stable (\(\epsilon = 0\)) harmonic oscillator can be destabilized with parametric forcing of very low amplitude if the forcing frequency is chosen carefully.
Vibrational Stabilization
\(-\omega^2\): The right combination of \(\epsilon\) and forcing frequency can stabilize the system.
\[ \ddot{x}(t) + (- \omega^2 + \epsilon \cos{t}) x(t) = 0 \]
Averaging methods relate the stability of a time varying system \(\dot{x} = f(t/\alpha, x)\) to that of a time-invariant averaged system \(\dot{x} = F(x)\) as \(\alpha \rightarrow 0\).
For Mathieu's Equation:
For \(\epsilon^2 > 2 \omega^2\), \(\exists\) \(\omega^{\star}\) such that the origin is (Lyapunov) stable for \(\omega < \omega^{\star}\)
Given a viable range of parameters \((\omega,\epsilon)\), how do they differ in performance?
How sensitive are different possible operating points of a parametric amplifier to noise?
How robust is the vibrationally stabilized region of parameter space to disturbances?
Addressing these questions requires understanding the input-output characteristics of the system
\[\ddot{x}(t) + (\pm \omega^2 + \epsilon \cos{t}) x(t) = \delta(t)\]
"Transfer function" analysis: Let \(\bar{\omega} = \frac{2\pi}{T}\). Then
Lifting allows us to represent a time-periodic system as a time invariant one in a space with higher dimensional input and output spaces
CONTINUOUS TIME, PERIODIC
where \(x(t) \in \mathbb{R}^n\), \(u(t) \in \mathbb{R}^p\), \(y(t) \in \mathbb{R}^m\).
DISCRETE TIME, LTI
where \(\hat{x}_k \in \mathbb{R}^n\), \(\hat{u}_k \in L_2^p[0,T]\), \(\hat{v}_k \in L_2^m[0,T]\).
\(f \in L_{N,e}^p[0,\infty)\), \(1 \le p < \infty\): Extended space of continuous time \(N\) -vector signals
\(\hat{f} \in l^p_{L^p[0,T]}\): Space of sequences which take values in \(L^p[0,T]\).
The lifting operator \(W_T: L^p[0,\infty) \rightarrow l^p_{L^p[0,T]}\), such that
\[\hat{f} = W_T f,\ \hat{f}_i(t) = f(T i + t),\ 0 \le t \le T\]
\(W_T\) breaks up the signal \(f\) into an infinite number of pieces, each of which is a shifted copy of \(f\) restricted to an interval \([0,T]\).
Let \(D_T\) and \(S\) be delay and shift operators:
Then \(W_T D_T = S W_T\)
For any linear operator \(G: L^p[0,\infty) \rightarrow L^p[0,\infty)\), define its lifting as \[ \hat{G} := W_T G W_T^{-1},\ \hat{G}:l^p_{L^p[0,T]}\rightarrow l^p_{L^p[0,T]} \]
If \(G\) is \(T\) -periodic, then it commutes with the delay operator \(D_T\), \(G D_T = D_T G\)
Then \(\hat{G}S = S \hat{G}\), so \(\hat{G}\) is shift-invariant (LTI)
CONTINUOUS TIME, PERIODIC
where \(x(t) \in \mathbb{R}^n\), \(u(t) \in \mathbb{R}^p\), \(y(t) \in \mathbb{R}^m\).
DISCRETE TIME, LTI
where \(\hat{x}_k \in \mathbb{R}^n\), \(\hat{u}_k \in L_2^p[0,T]\), \(\hat{v}_k \in L_2^m[0,T]\).
\(\hat{G}\) is shift-invariant (in \(k\)) and so has a simple solution: \[\hat{y}_k = \sum_{j=0}^{k-1} \hat{C} \hat{A}^{k-j-1} \hat{B} \hat{w}_j + \hat{D} \hat{w}_k \]
\(\hat{G}\) has the semi-infinite Toeplitz representation \[\hat{G} \equiv \begin{bmatrix} \hat{D} & 0 & 0 & 0 & \dots\\ \hat{C}\hat{B} & \hat{D} & 0 & 0 & \dots\\ \hat{C} \hat{A} \hat{B} & \hat{C}\hat{B} & \hat{D} & 0 & \dots\\ \hat{C} \hat{A}^2 \hat{B} & \hat{C} \hat{A} \hat{B} & \hat{C}\hat{B} & \hat{D} & \dots\\ \vdots & \ddots & \ddots & \ddots & \ddots\\ \end{bmatrix} \]
Discretized symplectically with \(N\) steps over period \(T\), then lifted
First order Euler backward/forward for the two components:
\(x_n = x(n \Delta),\quad f_n = f(n \Delta)\)
Symplectic:
\(\phi_k^T \left( \begin{smallmatrix} 0 & 1\\ -1 & 0\end{smallmatrix} \right)\phi_k = \left( \begin{smallmatrix} 0 & 1\\ -1 & 0\end{smallmatrix} \right)\)
Found as the eigenvalues of the Monodromy map \(\hat{A} = \Phi(T,0)\)
The Eigenvalues are constrained to the unit circle (when the system is stable) or the real axis (when unstable).
Since \(\Phi(0,0) = I\), \(\det \Phi(t,0) = 1 \forall t>0\)
\(\lambda_1 = \frac{1}{\lambda_2}\) and
\(\lambda_1^{\star} = \lambda_2\) or \(\lambda_1, \lambda_2 \in \mathbb{R}\)
The argument of the eigenvalues is always \(0\) or \(\pi\) at the stability boundaries. In the stable regions the poles are \(e^{\pm j \bar{\omega}}\), \(0 \le \bar{\omega} \le \pi\).
The lifted system has a single mode that grows at resonance. This suggests that its frequency response close to resonance is similar to that of a second order system.
Transfer function of \(\hat{G}\):
\( \hat{G}(z) = \underbrace{\hat{C} (z I - \hat{A})^{-1} \hat{B}}_{\text{rank } 2} + \underbrace{\hat{D}}_{\infty \text{ rank but finite and bounded}} \)
Near resonance, \((z I - \hat{A})^{-1}\) is near-singular and outstrips \(\hat{D}\) in magnitude, so it depends on \(z\) through factors \((z - e^{\pm j \bar{\omega}})^{-1}\):
\[\hat{Y}(z) \approx \frac{c \hat{u}_1(e^{j \bar{\omega}})}{z - e^{j \bar{\omega}}} + \frac{c^{\star} \hat{u}_1(e^{-j \bar{\omega}})}{z - e^{-j \bar{\omega}}} \]
\(\hat{u}_1(e^{j \bar{\omega}})\): First left singular vector at resonance
\(y(t) = p(t) \exp{\left(j \bar{\omega} \lfloor \frac{t}{T} \rfloor \right)},\ p(t+T) = p(t)\)
If \(\bar{\omega}\) is rational: periodic function, otherwise "almost periodic"
\(y(t) \stackrel{W_T}{\rightarrow} \hat{y}_k = p e^{j \bar{\omega} k}\)
\(\hat{y}_l = p e^{j \bar{\omega} (l-1)} \Leftrightarrow \hat{Y}(z) = \frac{p}{z - e^{j \bar{\omega}}}\)
\[\hat{Y}(z) \approx \frac{c \hat{u}_1(e^{j \bar{\omega}})}{z - e^{j \bar{\omega}}} + \frac{c^{\star} \hat{u}_1(e^{-j \bar{\omega}})}{z - e^{-j \bar{\omega}}} \Leftrightarrow y(t) \approx \Re \left[ c\ \hat{u}_{1,t}(e^{j \bar{\omega}})\ \exp \left\{j \bar{\omega} \left( \lfloor \frac{t}{T} \rfloor - 1 \right)\right\}\right] \]
The free response of Mathieu's Equation can be characterized as the product of copies of a "constant" function \(\hat{u}_1(e^{j \bar{\omega}})\) and an "almost periodic" complex exponential.
Impulse response (full numerical solution)
Low rank approximation (using first LSV)
The \(H_2\) norm is a measure of the system's input-amplification \[\left\lVert{G}\right\rVert_{H_2}^2 := Tr \left( \int_0^{\infty} G(t) G^{\star}(t) \mathrm{d}t \right)\] Square average of the norms of the responses to a set of unit inputs that excite all "parts" of the system.
The \(H_2\) norm is a measure of the system's input-amplification \[\left\lVert{G}\right\rVert_{H_2}^2 := Tr \left( \int_0^{\infty} G(t) G^{\star}(t) \mathrm{d}t \right)\] Square average of the norms of the responses to a set of unit inputs that excite all "parts" of the system.
When \(G\) is \(T\) -periodic,
\[ \left\lVert{G}\right\rVert_{H_2}^2 := \frac{1}{T} \int_0^T Tr \left( \int_0^{\infty} G(t,s)G^{\star}(t,s) \mathrm{d}s \right) \mathrm{d}t \]
Collects contributions from impulses applied at all times \(0 \le t \le T\).
Under the lifting \(W_T\), \(G \rightarrow \hat{G}\),
\(W = \sum_{k=1}^{\infty} \hat{A}^{k-1} \hat{B} \hat{B}^{\star} \hat{A}^{\star(k-1)} \Leftrightarrow \hat{A} W \hat{A}^{\star} - W = -\hat{B} \hat{B}^{\star}\)
Alternatively, the \(H_2\) norm is computed as the trace of the steady state error covariance when the system is fed zero-mean stationary white noise:
\(\lim_{t \rightarrow \infty} \frac{1}{T} \int_t^{t+T} Tr \left\{E [ y(\tau) y^{\star}(\tau) ]\right\} \mathrm{d}\tau\)
The input-output description of Mathieu's Equation is amenable to a low-rank approximation in the vibrationally stabilized region.
The free response of Mathieu's Equation can be approximated by the product of a periodic and an almost periodic function.
The \(H_2\) norm of Mathieu's Equation (external input to state) has a minimum in the vibrationally stabilized region.
In the stable region, the \(H_2\) norm achieves a minimum at some \(\epsilon \neq 0\) for each fixed \(\omega^2\).
Work submitted to The American Control Conference, 2020:
Other approaches:
The \(H_2\) norm is one measure of noise amplification.
How robust is Vibrational control? (\(H^{\infty}\) norm analysis)
Study phase noise in parametric oscillators
\[ w(t) \]
\( E \left[ w(t) \right] = \bar{w} \)
\( E \left[ w(t) w(t + \tau) \right] = R_w(\tau) \)
\[ \stackrel{G}{\longrightarrow}\]
\[ x(t) \]
\( E \left[ x(t) \right] = \bar{x}(t) \) where \( x(t+T) = x(t) \)
\( E \left[ x(t) x^{\star}(t+\tau) \right] = C_x(t,\tau) \) where \( C_x(t+T,\tau) = C_x(t,\tau) \)
Periodic systems produce cyclostationary output when the input is stationary.
\[\begin{array}{lcl} x(t) & \stackrel{W_T}{\longrightarrow} & \hat{x}_k\\ \bar{x}(t) := E \left[ x(t) \right] & \stackrel{W_T}{\longrightarrow} & \bar{\hat{x}} := E \left[ \hat{x}_k \right]\\ {C_x(t,\tau) := E \left[ x(t) x^{\star}(t+\tau) \right]} & \stackrel{W_T}{\longrightarrow} & {R_{\hat{x}}(l) := E \left[ \hat{x}_k \hat{x}_{k+l}^{\star} \right]}\\ \underbrace{S_x(\alpha,\omega):= \int_0^T \int_{\tau=-\infty}^{\infty} C_x(t,\tau) e^{- j \omega \tau} e^{-j \alpha t} \mathrm{d}\tau \mathrm{d}t}_{Cyclostationary} & \stackrel{W_T}{\longrightarrow} & \underbrace{P_{\hat{x}}(\theta) := \sum_l R_{\hat{x}}(l) e^{-l \theta}}_{Stationary} \end{array}\]
Is the output cyclostationary?
Distinguish between LTI and Time-Periodic dynamics from data
System identification of time-periodic systems