Feedforward Control of a Piezoelectric Flexure Stage for AFM

Feedforward Control of a Piezoelectric Flexure Stage for AFM
2008 American Control Conference
Westin Seattle Hotel, Seattle, Washington, USA
June 11-13, 2008
Feedforward Control of a Piezoelectric Flexure Stage for AFM
Yang Li and John Bechhoefer
Abstract— We review basic issues in the control of scanning probe microscopes. To improve the performance of the
present generation of instruments, we have developed a simple
feedforward technique that nonetheless increases the effective
bandwidth of the positioning stage by a factor of 15 over its
standard operation. If the desired control signal is known in
advance (as it is for a periodic scan signal), the feedforward
filter can be non-causal: information about the future can be
used to cancel the phase lag produced by the stage response.
We compare our design with other control techniques. We show
that model-based iterative control algorithms can lead to a substantial performance boost, at the cost of more measurements
of the system transfer function. We then introduce a model-free
variant that is simpler to set up, performs better, and is more
robust to system changes.
One of the clichés about the development of science is
the image of an ever-branching tree, where increasingly
specialized domains are viewed as new branches that are
connected to each other only by tracing back to the thicker
trunks of older growth. Thus, while the disciplines of physics
and control engineering share common origins in the study of
dynamical systems through the 19th century, their progress
in the 20th and 21st centuries has occurred largely in
separation from each other. Recently, however, the number of
connections between the two disciplines has been increasing
notably, as illustrated by two recent reviews by physicists
of applications of control theory to physics [1], [2]; by the
publication, by control engineers, of the first textbook on
modern control theory written for a broad audience of general
scientists and engineers [3]; and by the appearance of an
interesting “cross-over” work that analyzes physical systems
from a control perspective [4].
While influences have gone in both directions, we focus
here on applications of control theory to physics. Such
applications have come in two forms. First, central concepts
of control theory such as positive and negative feedback,
feedforward, and robustness have been recognized to be important in the understanding of complex systems, particularly
in biological applications [5], [6]. Second, while physicists
have for a long time limited themselves to using elementary
concepts from control theory, such as PID control, they have
recently begun to recognize the value of learning and applying more sophisticated concepts. Such applications have
been fruitful in the design of scanning probe microscopes,
particularly the atomic force microscope (AFM).
This work was supported by NSERC (Canada).
Dept. of Physics, Simon Fraser University, Burnaby, BC, V5A 1S6,
Canada. E-mail: [email protected]
978-1-4244-2079-7/08/$25.00 ©2008 AACC.
Much of the reason for the interest in applying control
concepts to AFM stems from the limitations of simple control
algorithms such as proportional-integral (PI) control [7]. The
dynamics of the AFM scan head (incorporating both the
mechanical resonances of the physical parts and the electromechanical response of the piezoelectric actuators) consist
of a number of weakly damped poles and of zeros that can
be in the right-hand plane (non-minimum phase systems).
Controlling such structures is notoriously difficult, and PI
algorithms are restricted to low bandwidths [8].
In the present article, we briefly review, in Sec. II, some
of the general issues in the control of AFMs. In fact, as
both Tien et al. [29] and Pao et al. [10] have emphasized,
the AFM is a MIMO system where the dynamics (and
control) of lateral scanners couples to the surface topography
sensor, often in significant ways. In this article, we shall
simplify by focusing mostly on the dynamics of the lateral
scanner. In Sec. III, we present a simple, inversion-based
feedforward control algorithm for AFM scanners. In Sec. IVA, we compare our method to a previously derived iterative
method. In Sec. IV-B, we introduce a “model-free” variant
with significant advantages.
Most current commercial AFMs are slow, with highquality images typically taking several minutes. Such slow
speeds can be traced back to the resonant frequencies of the
scanner (≈ 0.1–1 kHz) and the bandwidth of the combination
of cantilever sensor, vertical mechanical scanner, and control
system used to measure sample topography (≈ 1 kHz) –
“sensor,” for short. (The sensor bandwidth must exceed the
line-scan frequency by a factor equal to the number of pixels,
typically 100 to 1000 in each direction.) Higher-speed AFM
imaging is desirable, both because many applications require
imaging of fast processes and for operator comfort and
efficiency [11]. For example, to achieve “video” rates of 25
images/s at 200 pixels square implies scanner bandwidths of
at least 10 kHz (for a sinusoidal scan) and sensor bandwidths
of at least 2 MHz. For closed-loop operation and constantvelocity scans, these bandwidths would need to be multiplied
at least ten-fold. Such bandwidths are being approached in
recent instruments [12], [13], [14], [15].
It is useful to consider what sets the bandwidth in current AFMs. Since the control algorithms are scaled by the
characteristic frequencies (i.e., resonances), the bandwidth
is determined both by the physical resonance frequencies
and the ability of the control algorithm to perform well at
frequencies close to the resonances. While the focus here
is on the control aspects, we briefly review the physical
limitations. (See [16] for a more in-depth discussion.)
The physical frequency limits of AFM are associated
with the mechanical resonances of the lateral scanner, the
vertical scanner, and the cantilever sensor. Most commercial
AFMs have scanner dimensions of order cm and cantilevertip assemblies with lengths of order 100 µm, with scanner
resonances of order 100–1000 Hz and cantilever resonances
of order 10–100 kHz. Resonance frequencies ν are set by
the size and shape of objects: ν ∼ c/(ℓa), with c the sound
speed, ℓ the largest dimension, and a the aspect ratio (ratio
of largest length to smallest) [17]. Putting c ≈ 103 m/s,
ℓ ≈ 1 cm, and a ≈ 10 gives ≈ 100 Hz for the scanner;
with ℓ ≈ 100 µm and a ≈ 100 (typical for a commercial
cantilever), we get ≈ 100 kHz. One might naively think
that the aspect ratio should be 1 (cube-like structures), in
order to maximize the resonance frequency. But other factors
favor large aspect ratios. There is a maximum electric field
that piezoelectric materials can withstand, which leads to a
maximum displacement. The practical way to increase the
scan range is to put piezo elements in series, increasing
the aspect ratio. For cantilevers, larger aspect ratios make
softer probes, which minimize sample damage. The key,
then, to increasing the lowest resonance frequencies of both
scanner and cantilever is to use smaller overall length scales
and, as far as possible, lower aspect ratios. An interesting
new scanner design uses conventional parts and machining
but reduces ℓ and a to achieve resonances of 22 and 40
kHz (lateral and vertical directions) [18]. In parallel, smaller
cantilevers have been explored for a number of years [19]
and are beginning to be produced commerically [11]. The
ultimate solution would be to scale the entire AFM down to
a microfabricated chip [20].
Scaling down both scanner and tip sizes is only part
of the solution, however. In a typical commercial AFM,
scan speeds are limited to a few Hz, even though the
scanner resonances are a few hundred Hz. The ratio of the
scanning bandwidth to physical scanner resonance is thus
only ≈ 0.01. The feedforward technique to be presented
here allows one to increase this ratio easily to 0.1 and, with
more effort, to 0.3. Our technique [22], is only one of a
number of advanced control techniques for AFM that have
been discussed recently (reviewed in [10]), and we place our
scheme in the context of others, below.
In this section, we outline a simple, practical version
of feedforward to improve the scan rates of a commercial
piezoelectric flexure stage [21]. The design is an improved
version of one we presented recently [22]. The basic idea of
feedforward is to use the known dynamical characteristics of
a system to design a prefilter that “inverts” those dynamics
as far as possible, so that the output more closely resembles
the desired input. Our goal was to achieve reasonable performance while keeping the design as simple as possible. Simplicity has two virtues: first, the design is compatible with
many existing commercial AFMs; second, the design can
be understood and implemented by users without extensive
control-theory background. This latter reason was important,
as a secondary goal of our work was to proselytize the virtues
of using feedforward to the physics community, where it has
been seldom applied.
In our work, we implemented feedforward on a closedloop piezoelectric flexure stage [21]. Such stages are becoming more standard on commercial instruments and have
two advantages over the tube scanners of older AFMs: first,
they incorporate position sensors that allow the internal
feedback of the stage to compensate, at low-frequencies, for
the hysteresis and creep found in the open-loop response of
piezoelectric elements. Although there have been attempts
to invert the nonlinear, hysterestic models that describe
the piezo-actuator response, the models are difficult and
require extensive characterization of the scan response under
different conditions. Correcting as much of that motion in
feedback makes the feedforward task much easier [23]. A
second advantage of using a commercial flexure stage is
that the flexure mechanisms do a better job of decoupling
dynamics, particularly horizontal-vertical coupling [29].
Since the feedback in the translation stage is effective at
low frequencies in linearizing the stage response, we can
describe that response by a linear transfer function, T (s),
which represents the closed loop dynamics. Fig. 1 shows
the measured response of our stage. The two-pole, lowpass filter part of the dynamics, with a bandwidth of 27
Hz, arises from the feedback electronics of the stage. We
see here the origin of the small ratio of practical scanning
rates (scan lines/time) to resonance frequency. Because of
the lightly damped mechanical stage resonance at ≈ 440
Hz, it would be difficult to set the closed-loop bandwidth
higher. Without feedforward, scans should be at no more
than 10% of the feedback bandwidth, in order to prevent
attenuation and lag of the higher harmonics of the trianglewave scan signal. Thus, one arrives at scan rates of 2-3
lines/sec., which is typically the maximum used in AFMs
based on such scanners.
The feedforward design presented in [22] approximates
the inverse of the response shown in Fig. 1. It is important
to realize that an exact inverse, which would trivially lead to
exact tracking of the output by the input, is never possible.
First, the transfer function is never perfectly known. Second,
all stable poles become zeroes in the inverse. This means
that they are approximately differentiators and require a
frequency response that increases indefinitely at high frequencies. For example, a simple pole, 1/(1 + s), becomes
a simple zero, 1 + s, whose magnitude response increases
linearly with frequency at high frequencies. Such a response
cannot be exactly realized by any physical actuator, whose
response will always roll off at high-enough frequencies.
Third, if the system is non-minimum phase, then the inverse
has unstable poles. Devasia et al. have come up with clever
ways to deal with this situation, albeit at the cost of using a
non-causal prefilter [24]. Here, one of the benefits of using
a closed-loop translation stage is that its dynamics are well-
version of the pre-filter. Fig. 2 shows traces illustrating the
improvement for various scan speeds.
One limitation of using a feedforward pre-filter on a
closed-loop positioning stage is that the finite bandwidth of
the closed-loop controller (the low-pass response in Fig. 1)
limits the bandwidth obtainable by the pre-filter. In essence,
the modified input must be amplified at high frequences. At
frequencies that are too high, the amplification factor can
easily cause the desired stage input signal to be clipped by
its finite input range. One can, of course, restrict the range
and work in the central part of the overall scanner range.
But there will always be a practical limit. In our case, with
a bandwidth of 400 Hz., we could perform scans of 10 µm
at 150 Hz before being limited by the range of the piezo
Magnitude Response
with feedforward
stage only
4th-order model
Phase Delay (deg)
Frequency (Hz)
10 Hz
Fig. 1.
Magnitude (a) and phase (b) Bode plot of translation stage
response. Dashed curves are fit to a fourth-order model. The response after
compensation by the pre-filter is also shown.
described by a minimum-phase transfer function at relevant
frequencies (Fig. 1).
In our work, we arrive at a physically realizable response
by shifting the poles of the response function to higher
frequencies. These are kept below the first mechanical resonance but may be much closer than the closed-loop bandwidth. In the case considered here, the chosen bandwidth
was 400 Hz, which nearly equals the lowest mechanical
resonance of the stage, 430 Hz. We then constructed our
approximation to the transfer-function inverse by placing
zeros on top of each pole (covering the feedback poles
and the first mechanical resonance). Having put four zeroes
in our pre-filter (two for the feedback loop and two for
the mechanical resonance), we added four poles, in order
to keep the prefilter response finite at high frequencies.
The poles corresponding to the feedback were placed in
a Butterworth configuration giving the desired increased
bandwidth (400 Hz). The Butterworth configuration gives
the flattest amplitude response. The poles corresponding
to the mechanical resonance were left at the resonance
frequency, but the damping of that resonance was shifted to
be at critical damping. The continuous-time transfer function
was converted into a discrete-time version using the Tustin
(bilinear) transform [25], with a sampling rate of 10 kHz. Our
filter is implemented as an infinite-impulse-response (IIR)
filter, which requires fewer coefficients than the alternative,
finite-impulse-response (FIR) filters championed by Seering
and collaborators [26]. The IIR filter is of the form,
rn′ = a2 rn + a1 rn−1 + a0 rn−2 − b1 rn−1
− b0 rn−2
, (1)
with r the desired input and r′ the modified input, and where
the a and b coefficients are taken from the discrete-time
40 Hz
80 Hz
Fig. 2. Measured stage responses at (a) 10 Hz, (b) 40 Hz, and (c) 80
Hz. Dotted-line triangular waveform represents the desired stage response
(1 µm amplitude). Dashed line shows the phase shifted and attenuated stage
response without feedforward. Red lines show the response with a causal
feed-forward filter giving 400 Hz system bandwidth. Green lines show the
response with a similar non-causal filter.
One advantage of using a feedforward pre-filter is that
the repetitive nature of the desired triangle-wave scan allows
one to design a non-causal prefilter. Intuitively, with future
knowledge of the signal, one can send a control input to
the stage in advance of the desired movement, such that the
phase advance of each frequency component just cancels out
the delay due to the stage inertia. A simple way to compute
the desired waveform is sketched in Fig. 3, with the results
for the scan signal illustrated in Fig. 2. The basic idea is
to first pass the signal through the pre-filter and a model
of the system’s dynamics. This generates a conventional,
phase-delayed signal. That signal is then time reversed and
passed again through the pre-filter. All these steps are done
computationally. Finally the signal is passed through the
physical stage. The combined dynamics of pre-filter and
stage then remove the phase delays generated in the first
pass, resulting in a signal with zero-phase shift. In Fig. 2,
one can see clearly that the non-causal filter removes nearly
all phase shift relative to the reference. The small residual
shift noticeable at 80 Hz is due to the unmodeled dynamics
that begin to be important at 1 kHz.
In principle, in order to implement a non-causal filter
exactly, one should know the desired input signal out to
infinite times in the future. In practice, because the stage
dynamics decay, knowledge of the future to a specified
accuracy is required only to finite times. Zou and Devasia
have used this idea to develop a more sophisticated approach,
where a finite-time “preview” is all that is needed [27]. In
the case of AFM images, such an approach is typically not
needed, in that the scanning waveform is periodic, except
Then one starts with a control signal that, in the frequency
domain, is given by
u0 (jω) = G−1 (jω) yd (jω) ,
Fig. 3. Sketch of a non-causal feedforward filter. The light-shaded boxes
represent modeled dynamics, while the dark-shaded box represents the
physical system.
at the beginning and end of scanning. We thus generate
the required input waveform by inputting a waveform with
several (usually five) periods and then carrying out the rest
of the procedure illustrated in Fig. 3 with the steady-state
response. This gives a signal appropriate for a perfectly
periodic signal but which generates errors in the transient
when the first scan starts from rest. But simply adding a
scan line before starting the image proper easily removes
any effects of transient scanner dynamics. (Note here that
“transient” refers to the motion that occurs when the scanner
starts from rest and not to the motion occurring when the
stage reverses direction. The latter is corrected by the noncausal filter.)
The inversion-based design presented above is deliberately
simple and complements the more sophisticated techniques
presented within the control community. For example, a variant of optimal control tries to compensate for the uncertainties in the dynamics by using notions of robust control (H∞
metric) for the cost function [7], [28]. In another approach,
Tien, Zou, and Devasia [29] have explored feedforward
techniques where they supplement the kind of inversionbased techniques discussed here with a procedure inspired
by iterative learning control schemes [30], [31], [32]. The
basic idea is to apply a control repeatedly to a system, to
measure the outcome, and to use the difference between
observed and desired outcome to improve the control signal.
As Tien et al. note, such a scheme applies well to many
AFM operations, such as XY-stage scanning, where the
control signal is already periodic. They further note that
the periodicity makes it natural to formulate the iterative
method in frequency space. Here, we summarize the iterative
approach, implement it on our AFM stage, and compare its
performance to the simple inversion-based design discussed
above. We then go on to introduce a significant variation of
our own, which eliminates the preliminary modeling step.
A. Model-based iterative control
Here, we describe a variant of the iteration method introduced by Kim, Zou, and Su [33]. Because estimates of the
system transfer function (and related uncertainties) play a
key role in the algorithm, we refer to their method as “model
based.” Let the actual system transfer function be denoted by
G∗ (jω) and the experimentally determined model by G(jω).
where yd (jω) is a frequency component of the desired
sensor signal, yd (t). Here, one takes advantage of the fact
that a repetitive control process is naturally analyzed in the
frequency domain. One measures N periods of the repetitive
waveform, Fourier transforms, calculates the update for each
Fourier component, and the transforms the result back to the
time domain to find the desired control signal, u(t). After
applying the initial control signal, one measures the actual
output y(jω) and updates the control signal as
|uk | = |uk−1 | + ρ G−1 (|yd | − |yk−1 |) ,
6 uk
= 6 uk−1 + (6 yd − 6 yk−1 ) .
k ≥ 1 , (3)
In Eq. 3, all quantities are functions of jω. Kim et al.
show that the iteration law in Eq. 3 is stable provided
that the free parameter ρ is chosen to be in the range
0 < ρ < 2/∆G, where ∆G = G∗ /G is a measure of the
uncertainty in the determination of the transfer function at the
given frequency ω. One cannot directly measure ∆G since
G∗ is unknowable, but it may be estimated by the spread of
repeated measurements of the transfer function. Specifically,
we inject white noise for the transfer-function measurements
and take the ratio of the maximum to minimum values
over 10 runs, sampling at 10 kHz for 1 s. Below 500 Hz,
∆G < 1.2, increasing to 2 by 1 kHz. Above that frequency,
we truncate all harmonics in u.
Using the measured G (Fig. 1) and setting ρ = 1/∆G, we
obtain the measured 80 Hz waveforms and residuals shown
in Fig. 4(b) and (e). Comparing to the results obtained with
the inversion-based feedforward method (a) and (d), we see
a noticeable improvement, which we ascribe to two factors:
first, the slight phase shift due to modeling errors in the
inversion-based method is removed. Second, the bandwidth
of the iterative method was 1 kHz, in contrast to the 400
Hz bandwidth of the inversion-based method. Indeed, one
notes that an advantage of the iterative method is that it is
not limited by any particular bandwidth per se; rather, what
counts is how accurate the transfer-function measurement
is at any particular frequency. When ∆G ≫ 1, ρ will
be very small, and the combination of sensor and actuator
noise will prevent any meaningful convergence of u to its
optimal value. But this condition is independent of where
mechanical resonances lie. Of course, in general uncertainties
will be greatest near resonances, since they can be narrow
in piezoscanners and shift around in frequency as a function
of changing mechanical load, etc.
We also compared the driving wave forms for the methods.
Fig. 5(a) shows the non-causal inversion-based driving signal
(solid line). We note how it starts to alter the motion before
the output wave form reverses (dotted line). Fig. 5(b) shows
the same signals for the model-based interative scheme. The
large-amplitude, high-harmonic at 800 Hz does not appear
in the inversion-based input signal, as it is beyond the
3) We acquire data over a block of N periods to reduce
sensor and actuator noise.
4) We initialize with the naive transfer function, G = 1.
Putting all of this together, we arrive at the following control
1000 nm
= yd ,
25 nm
|yd |
, k ≥ 1,
|uk | = |uk−1 |
|yk−1 |
6 uk
= 6 uk−1 + (6 yd − 6 yk−1 ) .
5 ms
Fig. 4. Waveforms and residual tracking errors for three different control
algorithms. (a) Inversion-based feedforward; (b) Model-based iteration; (c)
Model-free iteration. Measured signal (green) is nearly perfectly superposed
on the tracking signal (red) in all three cases. (d-f) Residual tracking errors
for the respective control algorithms. 80 Hz, 1 µm peak-to-peak amplitude.
Fig. 5. Driving wave forms. (a) Causal inverse. (b) Non-causal inverse.
(c) Model-based iteration; (d) Model-free iteration. Dashed red line is the
measured sensor signal; solid green line is the actuator signal sent to the
stage. 80 Hz, 1 µm peak-to-peak amplitude.
While we have not investigated the theoretical convergence
properties of the model-free algorithm presented here, we
have observed that it performs slightly better than the modelbased iterative algorithm. In Fig. 4, parts (c) and (f) show
the sensor signal and residuals for our new algorithm. Both
waveforms look “perfect” at the scale drawn, while the
residuals for the model-free algorithm are slightly lower.
In Table I, we show the rms and sup-norm error for all
three methods. The model-free method has the lowest error
level, presumably because it eventually develops a model
estimate that is better than the one we used for the modelbased method. Both methods are substantially better than the
non-causal feedforward method presented above. The main
reason for the higher error comes from poorer tracking of
the corners (where the stage reverses its velocity). This is to
be expected, since its bandwidth was lower (0.4 vs. 1 kHz).
bandwidth of that controller. Its large amplitude here may
be mostly traced back to the low-pass filter in the analog
feedback loop of the piezo stage.
sensor & actuator noise
B. Model-free iterative control
While the model-based iterative control improves the
tracking error noticeably, it is at the cost of additional
complexity. One must first measure the transfer function
repeatedly, ideally under a wide range of circumstances
(including variations in driving amplitude and offset, stage
load, etc.) in order to estimate the uncertainties ∆G. Second,
the actual iteration scheme must be implemented. The second
concern is a one-time effort; the first is more worrisome in
the sense that entire set of possible experiments and their
consequential variations in G must be anticipated.
In order to circumvent these issues, we introduce a modelfree variation of the method of Kim et al..
1) We use the most recent input-output measurement to
estimate G−1 ≈ uk−1 /yk−1 .
2) We choose ρ = 1/∆G, the value that converges
most rapidly for linear, noiseless dynamics. (For weak
nonlinearity and noise, we do not anticipate that the
optimal value will shift greatly.)
E2 (%)
E∞ (%)
In Fig. 6, we show the iteration dependence of the rms
errors. The model-free algorithm was based on measuring 1
period/iteration, while the model-based algorithm was given
10 periods/iteration. (If we used only one period, it did not
converge.) As expected, the model-based algorithm initially
had a lower error, as its initial guess was based on previous
measurements of the transfer function while the model-free
algorithm takes yd as its initial guess. Still, after 5 iterations,
the model-free algorithm converged to an error level slightly
below that of the model-based algorithm.
We then tested the robustness of both algorithms. At
iteration 8, we added a mass of 200 gm, which lowered
the resonance from 440 to 400 Hz (10%). The model-free
algorithm recovered again in 3 iterations, while the modelbased algorithm recovered noticeably more slowly. This is
also to be expected: the model-based algorithm mis-estimates
the uncertainties of the now-altered transfer function. At
Fig. 6.
Tracking errors in the model-based and model-free iteration
algorithms. Control-signal updates are calculated using one period for the
model-free and ten periods for the model-based algorithms. Initial transient
error of the model-free algorithm converges to steady state after 3 iterations.
First arrow shows effects of adding a 200 gm mass to stage; second arrow
shows effects of doubling output amplitude to 2 µm. Waveform of 80 Hz.
iteration 21, we doubled the amplitude of the driving signal.
Again, the model-free algorithm recovered rapidly, while the
model-based algorithm was slower.
We also measured the effects of sensor and actuator noise
and found that each contributed about 0.35% rms relative
to the amplitude of driving wave form that we used. This
implies that systematic errors, most likely due to nonlinear
effects, account for the rest of the errors.
The motivation for introducing our inverse-based feedforward prefilter and applying it to a closed-loop scanning stage
was to combine the simplicity of working with a closed-loop
scanner with the larger bandwidths made possible by the use
of the approximate inverse to the closed-loop transfer function. Using a closed-loop stage helps circumvent the delicate
issues involved in modeling piezoelements [23]. We found
that a relatively simple transfer-function measurement could
be used to increase the effective bandwidth of the stage by
a factor of roughly 15, reaching nearly its main mechanical
resonance frequency. The inversion-based feedforward filter
is straightforward to set up and implement.
One issue with the inversion-based feedforward technique
is its lack of robustness. The inversion is helpful only to
the extent the transfer function is known accurately. Devasia
has addressed this issue [34] and found that the optimal
level of feedforward depends inversely on the uncertainty in
the system’s dynamics, for linear systems in the frequency
domain. The conclusion is that one should limit feedforward
to those frequencies where the dynamics are well known. In
the work presented above, the feedforward does this by using
a low-pass filter to turn off its effects for frequencies that
exceed the maximum used in the transfer-function model.
The iterative schemes that we addressed in the second part
of the paper take a fundamentally different approach, in that
one uses the measured output signal to reshape the input signal, forcing a convergence of the actual output to that desired.
In the formulations of Devasia, Zou, and collaborators, this
iteration is done offline and the result is implemented as a
feedforward filter. In the model-free variant presented here, it
is natural to implement the iteration continuously online. In
modern AFMs, one usually acquires the sensor signal from
the scanning stage anyway. The additional computational
effort to perform the forward and reverse Fourier transforms
is negligible, especially considering that one need update
only every scan periods. (Actually, for noise reduction and
better frequency resolution, one might well want to measure
– and hence update – in blocks of several periods.) Further,
modern AFM controllers use DSP and FPGA processors that
can easily run a variety of real-time processes in parallel. In
the work presented here, the iterations are done continuously
on an AFM scanner stage, but the software has yet to be
integrated with the rest of the AFM control program.
By performing the iterative scheme online, one gains all
the advantages of robustness illustrated above in Fig. 6.
Even for large changes in the driving signal or in the
transfer function, the iteration leads to a rapid recovery. The
robustness to changes in the transfer function are striking
in that they would render the inverse-based feedforward
scheme useless. Each mechanical resonance is “zeroed out,”
and a large change in the transfer function would require
further offline measurements. In the iterative scheme, they
are handled automatically, with no outside intervention. In
effect, the robustness here is reminiscent of that associated
with ordinary feedback. Here, the difference is that the
feedback is over slow time scales (several periods of the
driving frequency) and is thus easy to implement.
One challenge for the iterative control scheme is that
the higher bandwidths, which are an important factor in
the tracking improvements, require larger amplitudes for the
driving waveform. We can trace that back to the combination
of the closed-loop control, with its low-pass filter, and
the fact that we include frequencies above the principal
mechanical resonance of the system. Bypassing the stage’s
feedback loop and applying the control signal directly to
the piezos will partly resolve the problem by making the
system response roll off more slowly at high frequencies. Of
course, one then loses the advantages of having a feedback
controller eliminate the nonlinearities, hysteresis, creep, etc.
of the stage. These problems will be at least partially offset
by the iterative nature of the control algorithm.
Other issues with model-free iterative control that need
to be worked out include the effect of nonlinearities and
methods for correcting external disturbances.
We have presented a simple, practical feedforward design
that significantly improves the performance of commercial
piezoelectric translation stages used in atomic force microscopy and other applications of nanoscience. Increasing
the overall speed of AFM measurements requires revamping
many aspects, including both hardware and software, and the
work presented here is just one part of such an effort.
We then examined an alternate control based on iterative
learning algorithms. We showed that the model-based algo-
rithm of Devasia, Zou, and collaborators could substantially
improve the performance, at the cost of additional measurements and programming. We then introduced a model-free
variant of the iterative control method that slightly exceeded
the model-based algorithm in performance but was easier to
implement (no model to measure, no parameters to tune) and
more robust to changes in the system transfer function. This
method merits further study.
We began this contribution by noting the common view
of the development of science as a growing, ever-branching
tree. In this contribution, we have given an example of new
connections made between two branches that split away
from each other many years ago. Such “reconnections” have
become increasingly popular in many parts of science in the
guise of “interdisciplinary research,” where it is recognized
that progress on difficult problems requires contributions
from a variety of disciplines. Perhaps a more appropriate
image of the development of science might be that of a
complex, growing network, with numerous and unexpected
connections between different domains.
We gratefully acknowledge support from NSERC
(Canada). JB also thanks Lucy Pao, Qingze Zou, and the
other organizers of the AFM sessions of ACC 2008 for
encouraging cross-disciplinary dialog. We thank especially
the anonymous referee who suggested we compare our
inverse-based feedforward scheme to others.
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