value of the tunable parameters as the initial value. The call to rng seeds the random
number generator to produce a repeatable sequence of numbers.
systune displays the final result for each run. The displayed value, Soft, is the
maximum of the values achieved for each of the four performance goals. The software
chooses the best run overall, which is the run yielding the lowest value of Soft. The last
run fails to achieve closed-loop stability, which corresponds to Soft = Inf.
Examine the best achieved values of the soft constraints.
fSoft
fSoft =
1.1327 1.1327 0.5140 1.1327
Only req3, the stability margin requirement, is met for all frequencies. The other values
are close to, but exceed, 1, indicating violations of the goals for at least some frequencies.
Use viewGoal to visualize the tuned control system performance against the goals and to
determine whether the violations are acceptable. To evaluate specific open-loop or closed-
loop transfer functions for the tuned parameter values, you can use linearization
commands such as getIOTransfer and getLoopTransfer. After validating the tuned
parameter values, if you want to apply these values to the Simulink® model, you can use
writeBlockValue.
Input Arguments
st0 — Interface for tuning control systems modeled in Simulink
slTuner interface
Interface for tuning control systems modeled in Simulink, specified as an slTuner
interface.
If you specify parameter variation or linearization at multiple operating points when you
create st0, then systune performs robust tuning against all the plant models. If you
specify an uncertain (uss) model as a block substitution when you create st0, then
systune performs robust tuning, optimizing the parameters against the worst-case
parameter values. For more information about robust tuning approaches, see “Robust
systune