models in a model array. To restrict enforcement of the requirement, enter 2:4 in the
Only Models text box.
For more information about tuning for multiple models, see “Robust Tuning
Approaches” (Robust Control Toolbox).
Algorithms
Evaluating Tuning Goals
When you tune a control system, the software converts each tuning goal into a normalized
scalar value f(x). Here, x is the vector of free (tunable) parameters in the control system.
The software then adjusts the parameter values to minimize f(x) or to drive f(x) below 1 if
the tuning requirement is a hard constraint.
Step Rejection Goal aims to keep the gain from disturbance to output below the gain of
the reference model. The scalar value of the requirement f(x) is given by:
fx = WFsTdys,x ∞,
or its discrete-time equivalent. Here, Tdy(s,x) is the closed-loop transfer function of the
constrained response, and ⋅ ∞ denotes the H∞ norm (see norm). WF is a frequency
weighting function derived from the step-rejection profile you specify in the tuning goal.
The gain of WF roughly matches the inverse of the reference model for gain values within
60 dB of the peak gain. For numerical reasons, the weighting function levels off outside
this range, unless you specify a reference model that changes slope outside this range.
This adjustment is called regularization. Because poles of WF close to s = 0 or s = Inf
might lead to poor numeric conditioning for tuning, it is not recommended to specify
reference models with very low-frequency or very high-frequency dynamics.For more
information about regularization and its effects, see “Visualize Tuning Goals” on page 10-
187.
Implicit Constraints
This tuning goal also imposes an implicit stability constraint on the closed-loop transfer
function between the specified inputs to outputs, evaluated with loops opened at the
specified loop-opening locations. The dynamics affected by this implicit constraint are the
stabilized dynamics for this tuning goal. The Minimum decay rate and Maximum
natural frequency tuning options control the lower and upper bounds on these implicitly
constrained dynamics. If the optimization fails to meet the default bounds, or if the
10 Control System Tuning