Encyclopedia of Systems and Control

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Experiment Design and Identification for Control

  • Håkan HjalmarssonEmail author
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_103-2


The experimental conditions have a major impact on the estimation result. Therefore, the available degrees of freedom in this respect that are at the disposal to the user should be used wisely. This entry provides the fundamentals for the available techniques. We also briefly discuss the particulars of identification for model-based control, one of the main applications of system identification.


Adaptive experiment design Application-oriented experiment design Cramér-Rao lower bound Crest factor Experiment design Fisher information matrix Identification for control Least-costly identification Multisine Pseudorandom binary signal (PRBS) Robust experiment design 


The accuracy of an identified model is governed by:
  1. (i)

    Information content in the data used for estimation

  2. (ii)

    The complexity of the model structure

The former is related to the noise properties and the “energy” of the external excitation of the system and how it is distributed. In regard to (ii), a model structure which is not flexible enough to capture the true system dynamics will give rise to a systematic error, while an overly flexible model will be overly sensitive to noise (so-called overfitting). The model complexity is closely associated with the number of parameters used. For a linear model structure with n parameters modeling the dynamics, it follows from the invariance result in Rojas et al. (2009) that to obtain a model for which the variance of the frequency function estimate is less than 1∕γ over all frequencies, the signal-to-noise ratio, as measured by input energy over noise variance, must be at least nγ. With energy being power × time and as input power is limited in physical systems, this indicates that the experiment time grows at least linearly with the number of model parameters. When the input energy budget is limited, the only way around this problem is to sacrifice accuracy over certain frequency intervals. The methodology to achieve this in a systematic way is known as experiment design.

Model Quality Measures

The Cramér-Rao bound provides a lower bound on the covariance matrix of the estimation error for an unbiased estimator. With \(\hat {\theta }_N\in \mathbb {R}^n\) denoting the parameter estimate (based on N input–output samples) and θo the true parameters,
$$\displaystyle \begin{aligned} N\mathrm{E}\left[\left(\hat{\theta}_N-\theta_o\right)\left(\hat{\theta}_N-\theta_o\right)^T\right]\geq N\;I_F^{-1}(\theta_o,N) \end{aligned} $$
where \(I_F(\theta _o,N)\in \mathbb {R}^{n\times n}\) appearing in the lower bound is the so-called Fisher information matrix (Ljung, 1999). For consistent estimators, i.e., when \(\hat {\theta }_N\rightarrow \theta _o\) as N →, the inequality (1) typically holds asymptotically as the sample size N grows to infinity. The right-hand side in (1) is then replaced by the inverse of the per sample Fisher information IF(θo) :=limNIF(θo, N)∕N. An estimator is said to be asymptotically efficient if equality is reached in (1) as N →.

Even though it is possible to reduce the mean-square error by constraining the model flexibility appropriately, it is customary to use consistent estimators since the theory for biased estimators is still not well understood. For such estimators, using some function of the Fisher information as performance measure is natural.

General-Purpose Quality Measures

Over the years a number of “general-purpose” quality measures have been proposed. Perhaps the most frequently used is the determinant of the inverse Fisher information. This represents the volume of confidence ellipsoids for the parameter estimates and minimizing this measure is known as D-optimal design. Two other criteria relating to confidence ellipsoids are E-optimal design, which uses the length of the longest principal axis (the minimum eigenvalue of IF) as quality measure, and A-optimal design, which uses the sum of the squared lengths of the principal axes (the trace of \(I_F^{-1}\)).

Application-Oriented Quality Measures

When demands are high and/or experimentation resources are limited, it is necessary to tailor the experiment carefully according to the intended use of the model. Below we will discuss a couple of closely related application-oriented measures.

Average Performance Degradation

Let Vapp(θ) ≥ 0 be a measure of how well the model corresponding to parameter θ performs when used in the application. In finance, Vapp can, e.g., represent the ability to predict the stock market. In process industry, Vapp can represent the profit gained using a feedback controller based on the model corresponding to θ. Let us assume that Vapp is normalized such that minθVapp(θ) = Vapp(θo) = 0. That Vapp has minimum corresponding to the parameters of the true system is quite natural. We will call Vapp the application cost. Assuming that the estimator is asymptotically efficient, using a second-order Taylor approximation gives that the average application cost can be expressed as (the first-order term vanishes since θo is the minimizer of Vapp)
$$\displaystyle \begin{aligned} &\mathrm{E}\left[V_{\mathrm{app}}(\hat{\theta}_N)\right]\\ &\quad\approx \frac{1}{2}\mathrm{E}\Bigg[\left(\hat{\theta}_N-\theta_o\right)^TV_{\mathrm{app}}^{\prime\prime}(\theta_o)\left(\hat{\theta}_N-\theta_o\right)\Bigg]\\ &\quad= \frac{1}{2N}\mathrm{Tr}\left\{V_{\mathrm{app}}^{\prime\prime}(\theta_o)I_F^{-1}(\theta_o)\right\} \end{aligned} $$
This is a generalization of the A-optimal design measure, and its minimization is known as L-optimal design.

Acceptable Performance

Alternatively, one may define a set of acceptable models, i.e., a set of models which will give acceptable performance when used in the application. With a performance degradation measure defined of the type Vapp above, this would be a level set
$$\displaystyle \begin{aligned} {{\mathcal{E}_{\mathrm{app}}}}=\left\{\theta:\; V_{\mathrm{app}}(\theta)\leq \frac{1}{\gamma}\right\} \end{aligned} $$
for some constant γ > 0. The objective of the experiment design is then to ensure that the resulting estimate ends up in \({{\mathcal {E}_{\mathrm {app}}}}\) with high probability.

Design Variables

In an identification experiment, there are a number of design variables at the user’s disposal. Below we discuss three of the most important ones.

Sampling Interval

For the sampling interval, the general advice from an information theoretic point of view is to sample as fast as possible (Ljung, 1999). However, sampling much faster than the time constants of the system may lead to numerical issues when estimating discrete time models as there will be poles close to the unit circle. Downsampling may thus be required.


Generally speaking, feedback has three effects from an identification and experiment design point of view:
  1. (i)

    Not all the power in the input can be used to estimate the system dynamics when a noise model is estimated as a part of the input signal has to be used for the latter task; see Section 8.1 in Forssell and Ljung (1999). When a very flexible noise model is used, the estimate of the system dynamics then has to rely almost entirely on external excitation.

  2. (ii)

    Feedback can reduce the effect of disturbances and noise at the output. When there are constraints on the outputs, this allows for larger (input) excitation and therefore more informative experiments.

  3. (iii)

    The cross-correlation between input and noise/disturbances requires good noise models to avoid biased estimates (Ljung, 1999).

Strictly speaking, (i) is only valid when the system and noise models are parametrized separately. Items (i) and (ii) imply that when there are constraints on the input only, then the optimal design is always in open loop, whereas for output constrained only problems, the experiment should be conducted in closed loop (Agüero and Goodwin, 2007).

External Excitation Signals

The most important design variable is the external excitation, including the length of the experiment. Even for moderate experiment lengths, solving optimal experiment design problems with respect to the entire excitation sequence can be a formidable task. Fortunately, for experiments of reasonable length, the design can be split up in two steps:
  1. (i)

    First, optimization of the probability density function of the excitation

  2. (ii)

    Generation of the actual sequence from the obtained density function through a stochastic simulation procedure

More details are provided in section “Computational Issues.”

Experimental Constraints

An experiment is always subject to constraints, physical as well as economical. Such constraints are typically translated into constraints on the following signal properties:
  1. (i)

    Variability. For example, too high level of excitation may cause the end product to go off-spec, resulting in product waste and associated high costs.

  2. (ii)

    Frequency content. Often, too harsh movements of the inputs may damage equipment.

  3. (iii)

    Amplitudes. For example, actuators have limited range, restricting input amplitudes.

  4. (iv)

    Waveforms. In process industry, it is not uncommon that control equipment limits the type of signals that can be applied. In other applications, it may be physically possible to realize only certain types of excitation. See section “Waveform Generation” for further discussion.

It is also often desired to limit the experiment time so that the process may go back to normal operation, reducing, e.g., cost of personnel. The latter is especially important in the process industry where dynamics are slow. The above type of constraints can be formulated as constraints on the design variables in section “Design Variables” and associated variables.

Experiment Design Criteria

There are two principal ways to define an optimal experiment design problem:
  1. (i)

    Best effort. Here the best quality as, e.g., given by one of the quality measures in section “Model Quality Measures” is sought under constraints on the experimental effort and cost. This is the classical problem formulation.

  2. (ii)

    Least-costly. The cheapest experiment is sought that results in a predefined model quality. Thus, as compared to best effort design, the optimization criterion and constraint are interchanged. This type of design was introduced by Bombois and coworkers; see Bombois et al. (2006).

As shown in Rojas et al. (2008), the two approaches typically lead to designs only differing by a scaling factor.

Computational Issues

The optimal experiment design problem based on the Fisher information is typically non-convex. For example, consider a finite-impulse response model subject to an experiment of length N with the measured outputs collected in the vector
where \(E\in \mathbb {R}^N\) is zero-mean Gaussian noise with covariance matrix σ2IN×N. Then it holds that
$$\displaystyle \begin{aligned} I_F(\theta_o,N)=\frac{1}{\sigma^2}\Phi^T\Phi \end{aligned} $$
From an experiment design point of view, the input vector Open image in new window is the design variable, but with the elements of IF(θo, N) being a quadratic function of the input sequence, all typical quality measures become non-convex.

While various methods for non-convex numerical optimization can be used to solve such problems, they often encounter problems with, e.g., local minima. To address this, a number of techniques have been developed either where the problem is reparametrized so that it becomes convex or where a convex approximation is used. The latter technique is called convex relaxation and is often based on a reparametrization as well. We use the example above to provide a flavor of the different techniques.


If the input is constrained to be periodic so that u(t) = u(t + N), t = −n, …, −1, it follows that the Fisher information is linear in the sample correlations of the input. Using these as design variables instead of u results in that all quality measures referred to above become convex functions.

This reparametrization thus results in the two-step procedure discussed in section “External Excitation Signals”: First, the sample correlations are obtained from an optimal experiment design problem, and then an input sequence is generated that has this sample correlation. In the second step, there is a considerable freedom. Notice, however, that since correlations do not directly relate to the actual amplitudes of the resulting signals, it is difficult to incorporate waveform constraints in this approach. On the contrary, variance constraints are easy to incorporate.

Convex Relaxations

There are several approaches to obtain convex relaxations.

Using the per Sample Fisher Information

If the input is a realization of a stationary random process and the sample size N is large enough, IF(θo, N)∕N is approximately equal to the per sample Fisher matrix which only depends on the correlation sequence of the input. Using this approximation, one can now follow the same procedure as in the reparametrization approach and first optimize the input correlation sequence. The generation of a stationary signal with a certain correlation is a stochastic realization problem which can be solved using spectral factorization followed by filtering white noise sequence, i.e., a sequence of independent identically distributed random variables, through the (stable) spectral factor (Jansson and Hjalmarsson, 2005).

More generally, it turns out that the per sample Fisher information for linear models/systems only depends on the joint input/noise spectrum (or the corresponding correlation sequence). A linear parametrization of this quantity thus typically leads to a convex problem (Jansson and Hjalmarsson, 2005).

The set of all spectra is infinite dimensional, and this precludes a search over all possible spectra. However, since there is a finite-dimensional parametrization of the per sample Fisher information (it is a symmetric n × n matrix), it is also possible to find finite-dimensional sets of spectra that parametrize all possible per sample Fisher information matrices. Multisine with appropriately chosen frequencies is one possibility. However, even though all per sample Fisher information matrices can be generated, the solution may be suboptimal depending on which constraints the problem contains.

The situation for nonlinear problems is conceptually the same, but here the entire probability density function of the stationary process generating the input plays the same role as the spectrum in the linear case. This is a much more complicated object to parametrize.


An approach that can deal with amplitude constraints is based on a so-called lifting technique: Introduce the matrix U = uuT, representing all possible products of the elements of u. This constraint is equivalent to The idea of lifting is now to observe that the Fisher information matrix is linear in the elements of U and by dropping the rank constraint in (5) a convex relaxation is obtained, where both U and u (subject to the matrix inequality in (5)) are decision variables.

Frequency-by-Frequency Design

An approximation for linear systems that allows frequency-by-frequency design of the input spectrum and feedback is obtained by assuming that the model is of high order. Then the variance of an nth-order estimate, \(G(e^{i\omega },\hat {\theta }_N)\), of the frequency function can approximately be expressed as
$$\displaystyle \begin{aligned} \text{Var}\; G(e^{i\omega},\hat{\theta}_N)\approx \frac{n}{N}\; \frac{\Phi_v(\omega)}{\Phi_u(\omega)} \end{aligned} $$
(System Identification: An Overview) in the open-loop case (there is a closed-loop extension as well), where Φu and Φv are the input and noise spectra, respectively. Performance measures of the type (2) can then be written as
$$\displaystyle \begin{aligned} \int_{-\pi}^{\pi} W(e^{i\omega})\; \frac{\Phi_v(\omega)}{\Phi_u(\omega)}d\omega \end{aligned} $$
where the weighting W(e) ≥ 0 depends on the application. When only variance constraints are present, such problems can be solved frequency by frequency, providing both simple calculations and insight into the design.


We have used the notation IF(θo, N) to indicate that the Fisher information typically (but not always) depends on the parameter corresponding to the true system. That the optimal design depends on the to-be identified system is a fundamental problem in optimal experiment design. There are two basic approaches to address this problem which are covered below. Another important aspect is the choice of waveform for the external excitation signal. This is covered last in this section.

Robust Experiment Design

In robust experiment design, it is assumed that it is known beforehand that the true parameter belongs to some set, i.e., θo ∈ Θ. A minimax approach is then typically taken, finding the experiment that minimizes the worst performance over the set Θ. Such optimization problems are computationally very difficult.

Adaptive Experiment Design

The alternative to robust experiment design is to perform the design adaptively or sequentially, meaning that first a design is performed based on some initial “guess” of the true parameter, and then as samples are collected, the design is revised taking advantage of the data information. Interestingly, the convergence rate of the parameter estimate is typically sufficiently fast that for this approach the asymptotic distribution is the same as for the design based on the true model parameter (Hjalmarsson, 2009).

Waveform Generation

We have argued above that it is the spectrum of the excitation (together with the feedback) that determines the achieved model accuracy in the linear time-invariant case. In section “Using the per Sample Fisher Information,” we argued that a signal with a particular spectrum can be obtained by filtering a white noise sequence through a stable spectral factor of the desired spectrum. However, we have also in section “Experimental Constraints” argued that particular applications may require particular waveforms. We will here elaborate further on how to generate a waveform with desired characteristics.

From an accuracy point of view, there are two general issues that should be taken into account when the waveform is selected:
  • Persistence of excitation. A signal with a spectrum having n nonzero frequencies (on the interval (−π, π]) can be used to estimate at most n parameters. Thus, as is typically the case, if there is uncertainty regarding which model structure to use before the experiment, one has to ensure that a sufficient number of frequencies is excited.

  • The crest factor. For all systems, the maximum input amplitude, say A, is constrained. To deal with this from an experiment design point of view, it is convenient to introduce what is called the crest factor of a signal:
    $$\displaystyle \begin{aligned} C_r^2=\frac{\max_t u^2(t)}{\lim_{N\rightarrow\infty}\frac{1}{N}\sum_{t=1}^{N}u^2(t)} \end{aligned} $$
    The crest factor is thus the ratio between the squared maximum amplitude and the power of the signal. Now, for a class of signal waveforms with a given crest factor, the input power that can be used is upper-bounded by
    $$\displaystyle \begin{aligned} \lim_{N\rightarrow\infty}\frac{1}{N}\sum_{t=1}^{N}u^2(t)\leq \frac{A^2}{C_r^2} \end{aligned} $$
    However, the power is the integral of the signal spectrum, and since increasing the amplitude of the input signal spectrum will increase a model’s accuracy, cf. (6), it is desirable to use as much signal power as possible. By (7) we see that this means that waveforms with low crest factor should be used.
A lower bound for the crest factor is readily seen to be 1. This bound is achieved for binary symmetric signals. Unfortunately, there exists no systematic way to design a binary sequence that has a prescribed spectrum. However, the so-called arcsin law may be used. It states that the sign of a zero-mean Gaussian process with correlation sequence rτ gives a binary signal having correlation sequence \(\tilde {r}_\tau =2/\pi \arcsin (r_\tau )\). With \(\tilde {r}_\tau \) given, one can try to solve this relation for the corresponding rτ.

A crude, but often sufficient, method to generate binary sequences with desired spectral content is based on the use of pseudorandom binary signals (PRBS). Such signals (which are generated by a shift register) are periodic signals which have correlation sequences similar to random white noise, i.e., a flat spectrum. By resampling such sequences, the spectrum can be modified. It should be noted that binary sequences are less attractive when it comes to identifying nonlinearities. This is easy to understand by considering a static system. If only one amplitude of the input is used, it will be impossible to determine whether the system is nonlinear or not.

A PRBS is a periodic signal and can therefore be split into its Fourier terms. With a period of M, each such term corresponds to one frequency on the grid 2πkM, k = 0, …, M − 1. Such a signal can thus be used to estimate at most M parameters. Another way to generate a signal with period M is to add sinusoids corresponding to the above frequencies, with desired amplitudes. A periodic signal generated in this way is commonly referred to as a Multisine. The crest factor of a multisine depends heavily on the relation between the phases of the sinusoids. times the number of sinusoids. It is possible to optimize the crest factor with respect to the choice of phases (Rivera et al., 2009). There exist also simple deterministic methods for choosing phases that give a good crest factor, e.g., Schroeder phasing. Alternatively, phases can be drawn randomly and independently, giving what is known as random-phase multisines (Pintelon and Schoukens, 2012), a family of random signals with properties similar to Gaussian signals. Periodic signals have some useful features:
  • Estimation of nonlinearities. A linear time-invariant system responds to a periodic input signal with a signal consisting of the same frequencies but with different amplitudes and phases. Thus, it can be concluded that the system is nonlinear if the output contains other frequencies than the input. This can be explored in a systematic way to estimate also the nonlinear part of a system.

  • Estimation of noise variance. For a linear time-invariant system, the difference in the output between different periods is due entirely to the noise if the system is in steady state. This can be used to devise simple methods to estimate the noise level.

  • Data compression. By averaging measurements over different periods, the noise level can be reduced at the same time as the number of measurements is reduced.

Further details on waveform generation and general-purpose signals useful in system identification can be found in Pintelon and Schoukens (2012) and Ljung (1999).

Implications for the Identification Problem Per Se

In order to get some understanding of how optimal experimental conditions influence the identification problem, let us return to the finite-impulse response model example in section “Computational Issues.” Consider a least-costly setting with an acceptable performance constraint. More specifically, we would like to use the minimum input energy that ensures that the parameter estimate ends up in a set of the type (3). An approximate solution to this is that a 99 % confidence ellipsoid for the resulting estimate is contained in \({{\mathcal {E}_{\mathrm {app}}}}\). Now, it can be shown that a confidence ellipsoid is a level set for the average least-squares cost \(\mathrm {E}[V_{N}(\theta )]=\mathrm {E}[\|Y-\Phi \theta \|{ }^2]=\|\theta -\theta _o\|{ }_{\Phi ^T\Phi }^2+\sigma ^2\). Assuming the application cost Vapp also is quadratic in θ, it follows after a little bit of algebra (see Hjalmarsson 2009) that it must hold that
$$\displaystyle \begin{aligned} \mathrm{E}[V_{N}(\theta)]\geq \sigma^2\left(1+\gamma c V_{\mathrm{app}}(\theta)\right),\; \forall \theta \end{aligned} $$
for a constant c that is not important for our discussion. The value of \(\mathrm {E}[V_{N}(\theta )]=\|\theta -\theta _o\|{ }_{\Phi ^T\Phi }^2+\sigma ^2\) is determined by how large the weighting ΦT Φ is, which in turn depends on how large the input u is. In a least-costly setting with the energy ∥u2 as criterion, the best solution would be that we have equality in (8). Thus we see that optimal experiment design tries to shape the identification criterion after the application cost. We have the following implications of this result:
  1. (i)

    Perform identification under appropriate scaling of the desired operating conditions. Suppose that Vapp(θ) is a function of how the system outputs deviate from a desired trajectory (determined by θo). Performing an experiment which performs the desired trajectory then gives that the sum of the squared prediction errors are an approximation of Vapp(θ), at least for parameters close to θo. Obtaining equality in (8) typically requires an additional scaling of the input excitation or the length of the experiment. The result is intuitively appealing: The desired operating conditions should reveal the system properties that are important in the application.

  2. (ii)

    Identification cost for application performance. We see that the required energy grows (almost) linearly with γ, which is a measure of how close to the ideal performance (using the true parameter θo) we want to come. Furthermore, it is typical that as the performance requirements in the application increase, the sensitivity to model errors increases. This means that Vapp(θ) increases, which thus in turn means that the identification cost increases. In summary, the identification cost will be higher, the higher performance that is required in the application. The inequality (8) can be used to quantify this relationship.

  3. (iii)

    Model structure sensitivity. As Vapp will be sensitive to system properties important for the application, while insensitive to system properties of little significance, with the identification criterion VN matched to Vapp, it is only necessary that the model structure is able to model the important properties of the system.

    In any case, whatever model structure that is used, the identified model will be the best possible in that structure for the intended application. This is very different from an arbitrary experiment where it is impossible to control the model fit when a model of restricted complexity is used.

    We conclude that optimal experiment design simplifies the overall system identification problem.


Identification for Control

Model-based control is one of the most important applications of system identification. Robust control ensures performance and stability in the presence of model uncertainty. However, the majority of such design methods do not employ the parametric ellipsoidal uncertainty sets resulting from standard system identification. In fact only in the last decade analysis and design tools for such type of model uncertainty have started to emerge, e.g., Raynaud et al. (2000) and Gevers et al. (2003).

The advantages of matching the identification criterion to the application have been recognized since long in this line of research. For control applications this typically implies that the identification experiment should be performed under the same closed-loop operation conditions as the controller to be designed. This was perhaps first recognized in the context of minimum variance control (see Gevers and Ljung 1986) where variance errors were the concern. Later on this was recognized to be the case also for the bias error, although here pre-filtering can be used to achieve the same objective.

To account for that the controller to be designed is not available, techniques where control and identification are iterated have been developed, cf. adaptive experiment design in section “Adaptive Experiment Design.” Convergence of such schemes has been established when the true system is in the model set but has proved out of reach for models of restricted complexity.

In recent years, techniques integrating experiment design and model predictive control have started to appear. A general-purpose design criterion is used in Rathouský and Havlena (2013), while Larsson et al. (2013) uses an application-oriented criterion.

Summary and Future Directions

When there is the “luxury” to design the experiment, then this opportunity should be seized by the user. Without informative data there is little that can be done. In this exposé we have outlined the techniques that exist but also emphasized that a well-conceived experiment, reflecting the intended application, significantly can simplify the overall system identification problem.

Further developments of computational techniques are high on the agenda, e.g., how to handle time-domain constraints and nonlinear models. To this end, developments in optimization methods are rapidly being incorporated. While, as reported in Hjalmarsson (2009), there are some results on how the identification cost depends on the performance requirements in the application, further understanding of this issue is highly desirable. Theory and further development of the emerging model predictive control schemes equipped with experiment design may very well be the direction that will have most impact in practice. Nonparametric kernel methods have proven to be highly competitive estimation methods and developments of experiment design methods for such estimators only started recently in the system identification community; see, e.g., Fujimoto et al. (2018).




This work was supported by the European Research Council under the advanced grant LEARN, contract 267381, and by the Swedish Research Council, contracts 2016-06079.


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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer Science, Centre for Advanced BioproductionKTH Royal Institute of TechnologyStockholmSweden

Section editors and affiliations

  • Lennart Ljung
    • 1
  1. 1.Division of Automatic Control, Department of Electrical EngineeringLinköping UniversityLinköpingSweden