In Control System Toolbox™, System Identification Toolbox™, and Robust Control Toolbox™ software, you represent linear systems as model objects. In System Identification Toolbox, you also represent nonlinear models as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays.
Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems.
The main families of model objects are:
Numeric Models — Basic representation of linear systems with fixed numerical coefficients. This family also includes identified models that have coefficients estimated with System Identification Toolbox software.
Generalized Models — Representations that combine numeric coefficients with tunable or uncertain coefficients. Generalized models support tasks such as parameter studies or compensator tuning.
The data encapsulated in your model object depends on the model type you use. For example:
Transfer functions store the numerator and denominator coefficients
State-space models store the A, B, C, and D matrices that describe the dynamics of the system
PID controller models store the proportional, integral, and derivative gains
Other model attributes stored as model data include time units, names for the model inputs or outputs, and time delays. For more information about setting and retrieving model attributes, see Model Attributes.
Note: All model objects are MATLAB® objects, but working with them does not require a background in object-oriented programming. To learn more about objects and object syntax, see Role of Classes in MATLAB (MATLAB) in the MATLAB documentation.