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Refine Controller Tuning Weights Using the Tuning Advisor

The Tuning Advisor can help you to refine controller tuning weights for better performance. It also provides a quantitative performance measurement.

You can access the Tuning Advisor from the Scenarios node in the Control and Estimation Tools Manager. Before you use the Advisor, choose the controller horizons and sampling period, specify constraints, and select a disturbance estimator (if the default estimator is inappropriate). The Advisor does not provide help with these parameters.

The example considered here is a plant with four controlled outputs and four manipulated variables. There are no measured disturbances and the unmeasured disturbances are unmodeled.

After starting the design tool and importing the plant model, G, which becomes the controller design basis, we accept the default values for all controller parameters. We also load a second plant model, Gp, in which all parameters of G have been perturbed randomly with a standard deviation of 5%.

The scenario shown in the previous figure specifies the controller based on G and the plant Gp. In other words, it tests the controllers robustness with respect to plant-model mismatch. It also defines a series of setpoint changes and disturbances.

Clicking Tuning Advisor opens the MPC Tuning Advisor window. In the Tuning Advisor window, we specify the following settings:

  • Select the IAE performance function (an arbitrary choice for illustration only).

  • Set all input performance weights to zero because the application does not have input targets.

  • Set all input rate performance weights to zero because the application has no cost for manipulated variable movement.

  • Leave the output performance weights at their default values (unity) because all controller outputs are of roughly equal magnitude and the application gives equal priority to the tracking of all four setpoints.

  • Click Baseline.

  • Click Analyze.

The Tuning Advisor resembles the previous figure. The sensitivity values indicate that a decrease in the Out4 weight or an increase in the Out2 weight would have the most impact. In general, however, the output tuning weights should reflect the setpoint tracking priorities and it's preferable to adjust the input rate tuning weights.

Sensitivities for Input Rate Weights In1 and In4 are of roughly equal magnitude but the In4 suggestion is a decrease and this weight is already near its lower bound of zero. Thus, we focus on the In1 weight.

The next figure shows the Advisor after the In1 weight has been increased in several steps from 0.1 to 4. Performance has improved by nearly 20% relative to the baseline. Sensitivities indicate that further adjustments to in input rate tuning weights will have little impact.

At this point, we can consider adjusting the output tuning weights. It is possible that an attempt to control a particular output might be causing upsets in other outputs (because of model error).

The next figure shows the Tuning Advisor after additional adjustments. At this point, some sensitivities are still rather large, but a small change in the indicated tuning weight causes the sensitivity to change sign. Therefore, futher progress will be difficult.

Overall, we have improved the performance by (26.69 − 20.14)/26.69 which is more than 20%.

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