Black-box modeling is useful when your primary interest is in fitting the data regardless of a particular mathematical structure of the model. The toolbox provides several linear and nonlinear black-box model structures, which have traditionally been useful for representing dynamic systems. These model structures vary in complexity depending on the flexibility you need to account for the dynamics and noise in your system. You can choose one of these structures and compute its parameters to fit the measured response data.
Black-box modeling is usually a trial-and-error process, where you estimate the parameters of various structures and compare the results. Typically, you start with the simple linear model structure and progress to more complex structures. You might also choose a model structure because you are more familiar with this structure or because you have specific application needs.
The simplest linear black-box structures require the fewest options to configure:
Estimation of some of these structures also uses noniterative estimation algorithms, which further reduces complexity.
You can configure a model structure using the model order. The definition of model order varies depending on the type of model you select. For example, if you choose a transfer function representation, the model order is related to the number of poles and zeros. For state-space representation, the model order corresponds to the number of states. In some cases, such as for linear ARX and state-space model structures, you can estimate the model order from the data.
If the simple model structures do not produce good models, you can select more complex model structures by:
Specifying a higher model order for the same linear model structure. A higher model order increases the model flexibility for capturing complex phenomena. However, an unnecessarily high order can make the model less reliable.
Explicitly modeling the noise by including the He(t) term, as shown in the following equation.
y(t) = Gu(t) + He(t)
Here, H models the additive disturbance by treating the disturbance as the output of a linear system driven by a white noise source e(t).
Using a model structure that explicitly models the additive disturbance can help to improve the accuracy of the measured component G. Furthermore, such a model structure is useful when your main interest is using the model for predicting future response values.
Using a different linear model structure.
Using a nonlinear model structure.
Nonlinear models have more flexibility in capturing complex phenomena than linear models of similar orders. See Nonlinear Model Structures.
Ultimately, you choose the simplest model structure that provides the best fit to your measured data. For more information, see Estimating Linear Models Using Quick Start.
Regardless of the structure you choose for estimation, you can simplify the model for your application needs. For example, you can separate out the measured dynamics (G) from the noise dynamics (H) to obtain a simpler model that represents just the relationship between y and u. You can also linearize a nonlinear model about an operating point.
A linear model is often sufficient to accurately describe the system dynamics and, in most cases, a best practice is to first try to fit linear models. If the linear model output does not adequately reproduce the measured output, you might need to use a nonlinear model.
You can assess the need to use a nonlinear model structure by plotting the response of the system to an input. If you notice that the responses differ depending on the input level or input sign, try using a nonlinear model. For example, if the output response to an input step up is faster than the response to a step down, you might need a nonlinear model.
Before building a nonlinear model of a system that you know is nonlinear, try transforming the input and output variables such that the relationship between the transformed variables is linear. For example, consider a system that has current and voltage as inputs to an immersion heater, and the temperature of the heated liquid as an output. The output depends on the inputs through the power of the heater, which is equal to the product of current and voltage. Instead of building a nonlinear model for this two-input and one-output system, you can create a new input variable by taking the product of the current and voltage and building a linear model that describes the relationship between power and temperature.
If you cannot determine variable transformations that yield a linear relationship between input and output variables, you can use nonlinear structures such as nonlinear ARX or Hammerstein-Wiener models. For a list of supported nonlinear model structures and when to use them, see Nonlinear Model Structures.
You can use the System Identification app or commands to estimate linear and nonlinear models of various structures. In most cases, you choose a model structure and estimate the model parameters using a single command.
Consider the mass-spring-damper system described in Dynamic Systems and Models. If you do not know the equation of motion of this system, you can use a black-box modeling approach to build a model. For example, you can estimate transfer functions or state-space models by specifying the orders of these model structures.
A transfer function is a ratio of polynomials:
For the mass-spring damper system, this transfer function is
which is a system with no zeros and 2 poles.
In discrete-time, the transfer function of the mass-spring-damper system can be
where the model orders correspond to the number of coefficients
of the numerator and the denominator (
nb = 1 and
2) and the input-output delay equals the lowest order exponent of z–1 in
the numerator (
nk = 1).
In continuous time, you can build a linear transfer function model using the
m = tfest(data,2,0)
data is your measured input-output data, represented as an
iddata object, and the model order is the set of number of poles (2) and the
number of zeros (0).
Similarly, you can build a discrete-time model Output Error structure using the
m = oe(data,[1 2 1])
The model order is [
nb nf nk] = [
1 2 1]. Usually, you do
not know the model orders in advance. Try several model order values until you find the
orders that produce an acceptable model.
m = ssest(data,2)
Here, the second argument
2 represents the order, or the number of states
in the model.
In black-box modeling, you do not need the equation of motion for the system — only a guess of the model orders.