Model reduction

Model reduction refers to a reduction of complexity, sometimes by approximation, of a model by a simpler model. Many control-design workflows benefit from model reduction, some examples are

A plant model is derived by linearization of a detailed model implemented in ModelingToolkit. Such a model may contain 1000s of states, but may be accurately described in a particular operating point by a low-order linear model.
When only subsets of all inputs and outputs of a large MIMO model are required, some states of the original model may no longer be required.
Some control-design techniques, such as $\mathcal{H}_\infty$ design or glover_mcfarlane may result in high-order controllers. Such a controller may sometimes be reduced while maintaining most of the stability margin.
Some modes in a large model may be unobservable or uncontrollable.

Reduced-order models require less effort to simulate and may improve the numerical performance of some algorithms. Model reduction of linear time-invariant models is a well-developed field, and this page lists some of the available functionality. For reduction of nonlinear models, consider a linearization-based approach, or build a nonlinear surrogate model.

Model reduction using balanced truncation is available through the functions

Model reduction GUI, available as JuliaSimControl.app_modelreduction.
minreal obtain a minimal realization containing only controllable and observable modes.
sminreal obtain a structurally minimal realization of a state-space model.
baltrunc2 for standard model reduction.
frequency_weighted_reduction for reduction with frequency focus.
baltrunc_coprime for normalized-coprime factor reduction.
baltrunc_unstab for unstable models.
controller_reduction reduce the order of controllers with guaranteed stability margins. See example.

Reduction of very large models

Balanced truncation requires the solution to a Lyapunov equation which may be prohibitively expensive for large systems. For systems of order above about 500, a method based on frequency-domain fitting may be substantially faster if the desired model order is less than about 100. The following example illustrates the procedure.

using JuliaSimControl, ControlSystemIdentification, Plots
ny,nu,nx = 5,5,1000                     # number of outputs, inputs and states
Ts = 1                                  # Sample time
G = ssrand(ny,nu,nx; Ts, proper=true);  # Generate a random system

N = 200                                 # Number of frequency points
w = range(0, stop=pi/Ts-1/N, length=N)  # Frequency vector

frd = FRD(w, G);                        # Build a frequency-response data object
nxr = 60                                # Reduced model order
@time Gh, x0 = subspaceid(frd, G.Ts, nxr; r=nxr+1, zeroD=true); # Fit frequency response

sigmaplot([G, Gh], w[2:end], lab=["Full order" "Reduced order"])

The frequency-fitting method does not have support for exact DC matching like the balanced-truncation method does, but there exists an option for frequency-based weighting which can achieve similar results. See subspaceid for additional details.