Why Scaling Is Important
When working with state-space models, proper scaling is important for accurate computations. A state-space model is well scaled when the following conditions exist:
The entries of the A, B, and C matrices are homogenous in magnitude.
The model characteristics are insensitive to small perturbations in A, B, and C (in comparison to their norms).
Working with poorly scaled models can cause your model a severe loss of accuracy and puzzling results. An example of a poorly scaled model is a dynamic system with two states in the state vector that have units of light years and millimeters. Such disparate units may introduce both very large and very small entries into the A matrix. Over the course of computations, this mix of small and large entries in the matrix could destroy important characteristics of the model and lead to incorrect results.
For more information on the harmful affects of a poorly scaled model, see Scaling Models to Maximize Accuracy.
When to Scale Your Model
You can avoid scaling issues altogether by carefully selecting units to reduce the spread between small and large coefficients.
In general, you do not have to perform your own scaling when using the Control System Toolbox™ software. The algorithms automatically scale your model to prevent loss of accuracy. The automated scaling chooses a frequency range to maximize accuracy based on the dominant dynamics of the model.
In most cases, automated scaling provides high accuracy without your intervention. For some models with dynamics spanning a wide frequency range, however, it is impossible to achieve good accuracy at all frequencies and some tradeoff of accuracy in different frequency bands is necessary. In such cases, a warning alerts you of potential inaccuracies. If you receive this warning, evaluate the tradeoffs and consider manually adjusting the frequency interval where you most need high accuracy. For information on how to manually scale your model, see Manually Scaling Your Model.