Table Data Tuning

Tune matrix or multi-dimensional table values

You can estimate matrix or multi-dimensional table values from measured data. You can also specify design requirements at the command line, and optimize the table values.

Functions

`sdo.requirements.FunctionMatching`	Impose function matching constraint on variable
`sdo.requirements.MonotonicVariable`	Impose monotonic constraint on variable
`sdo.requirements.PhasePlaneEllipse`	Impose elliptic bound on phase plane trajectory of two signals
`sdo.requirements.PhasePlaneRegion`	Impose region bound on phase plane trajectory of two signals
`sdo.requirements.RelationalConstraint`	Impose relational constraint on pair of variables
`sdo.requirements.SmoothnessConstraint`	Impose bounds on gradient magnitude of variable
`evalRequirement`	Evaluate design requirement

Topics

How to Estimate Lookup Table Values
Estimating lookup table values is an example of estimating parameters which are matrices or multi-dimensional arrays.
Estimate Lookup Table Values from Data
This example shows how to estimate lookup table values from time-domain input-output (I/O) data in the Parameter Estimator.
Estimate Constrained Values of a Lookup Table
This example shows how to estimate constrained values of a lookup table in the Parameter Estimator.
Design Optimization Using Lookup Table Requirements for Gain Scheduling (Code)
Impose design requirements on the parameters in a lookup table and then tune the parameters.
Design Optimization Using Lookup Table Requirements for Gain Scheduling (GUI)
Impose design requirements on the parameters in a lookup table in the Response Optimizer, and tune the parameters.