What Is Batch Linearization?
Batch linearization refers to extracting multiple linearizations from a model for various
combinations of I/Os, operating points, and parameter values. Batch linearization lets you
analyze the time-domain, frequency-domain, and stability characteristics of your Simulink
model, or portions of your model, under varying operating conditions and parameter
ranges. You can use the results of batch linearization to design controllers that are robust
against parameter variations, or to design gain-scheduled controllers for different
operating conditions. You can also use batch linearization results to implement linear
parameter varying (LPV) approximations of nonlinear systems using the LPV System
block of Control System Toolbox.To understand different types of batch linearization, consider the magnetic ball levitation
model, magball. For more information about this model, see “magball Simulink Model”.You can batch linearize this model by varying any combination of the following:- I/O sets — Linearize a model using different I/Os to obtain any closed-loop or open-
loop transfer function.
For the magball model, some of the transfer functions that you can extract by
specifying different I/O sets include:- Magnetic ball plant model, controller model
- Closed-loop transfer function from the Reference Signal to the plant output, h
- Open-loop transfer function for the controller and magnetic ball plant combined;
that is, the transfer function from the Error Signal to h with the feedback loop
opened - Output disturbance rejection model or sensitivity transfer function, obtained at the
outport of Magnetic Ball Plant block - Operating points — In nonlinear models, the model dynamics vary depending on the
operating conditions. You can linearize a nonlinear model at different operating points
3 Batch Linearization