The representation of a model in state-space is not unique. Coordinate transformation yields state-space models with different matrices but identical dynamics. State coordinate transformation can be useful for achieving minimal realizations of state-space models, or for converting canonical forms for analysis and control design.
Coordinate transformation can also be useful for scaling poorly-conditioned models. Proper scaling of state-space models is important for accurate computations. 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.
|Balanced state-space realization
|Optimal scaling of state-space models
|Compute modal state-space realization (Since R2023b)
|Compute companion state-space realization (Since R2023b)
|State coordinate transformation for state-space model
|Equivalence transformation for state-space models (Since R2023b)
|Reorder states in state-space models
|Sort states based on state partition (Since R2020b)
|Eliminate states from state-space models (Since R2023b)
|Append state vector to output vector
|Controllability of state-space model
|Observability of state-space model
|Controllability and observability Gramians
- State-Space Realizations
A state-space model can be expressed in an infinite number of realizations. Common forms, sometimes called canonical forms, include modal, companion, observable, and controllable forms.
- Scaling State-Space Models
When working with state-space models, proper scaling is important for accurate computations.
- Scaling State-Space Models to Maximize Accuracy
This example shows that proper scaling of state-space models can be critical for accuracy and provides an overview of automatic and manual rescaling tools.