to study how model dynamics vary or to design controllers for different operating
conditions.For an example of model dynamics that vary depending on the operating point,
consider a simple unforced hanging pendulum with angular position and velocity as
states. This model has two equilibrium points, one when the pendulum hangs
downward, which is stable, and another when the pendulum points upward, which is
unstable. Linearizing close to the stable operating point produces a stable model,
whereas linearizing this model close to the unstable operating point produces an
unstable model.For the magball model, which uses the ball height as a state, you can obtain multiple
linearizations for varying initial ball heights.- Parameters — Parameters configure a Simulink model in several ways. For example,
you can use parameters to specify model coefficients or controller sample times. You
can also use a discrete parameter, such as the control input to a Multiport Switch
block, to control the data path within a model. Therefore, varying a parameter can
serve a range of purposes, depending on how the parameter contributes to the model.
For the magball model, you can vary the parameters of the PID Controller block,
Controller/PID Controller. The linearizations obtained by varying these
parameters show how the controller affects the control-system dynamics. Alternatively,
you can vary the magnetic ball plant parameter values to determine the controller
robustness to variations in the plant model. You can also vary the parameters of the
input block, Desired Height, and study the effects of varying input levels on the
model response.If the parameters affect the model operating point, you can batch trim the model using
the parameter samples and then batch linearize the model at the resulting operating
points.See Also
LPV System
More About
- “Choose Batch Linearization Methods” on page 3-5
- “Batch Linearization Efficiency When You Vary Parameter Values” on page 3-10
See Also