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A model predictive controller design usually requires some tuning of the cost function weights. This topic provides tuning tips. See Optimization Problem for details on the cost function equations.

Before tuning the cost function weights, specify scale factors for each plant input and output variable. Hold these scale factors constant as you tune the controller. See Specify Scale Factors for more information.

During tuning, use the

`sensitivity`

and`review`

commands to obtain diagnostic feedback. The`sensitivity`

command is intended to help with cost function weight selection.Change a weight by setting the appropriate controller property, as follows:

To change this weight Set this controller property Array size OV reference tracking ( *w*)^{y}`Weights.OV`

*p*-by-*n*_{y}MV reference tracking ( *w*)^{u}`Weights.MV`

*p*-by-*n*_{u}MV increment suppression ( *w*)^{Δu}`Weights.MVRate`

*p*-by-*n*_{u}

Here, MV is a plant manipulated variable, and
*n _{u}* is the number of MVs. OV is a plant output
variable, and

If a weight array contains *n* < *p* rows, the
controller duplicates the last row to obtain a full array of *p* rows. The
default (*n* = 1) minimizes the number of parameters to be tuned, and is
therefore recommended. See Time-Varying Weights and Constraints for an
alternative.

Considering the

*n*OVs, suppose that_{y}*n*must be held at or near a reference value (setpoint). If the_{yc}*i*th OV is not in this group, set`Weights.OV(:,i)`

= 0.If

*n*≥_{u}*n*, it is usually possible to achieve zero OV tracking error at steady state, if at least_{yc}*n*MVs are not constrained. The default_{yc}`Weights.OV = ones(1,ny)`

is a good starting point in this case.If

*n*>_{u}*n*, however, you have excess degrees of freedom. Unless you take preventive measures, therefore, the MVs may drift even when the OVs are near their reference values._{yc}The most common preventive measure is to define reference values (targets) for the number of excess MVs you have,

*n*–_{u}*n*. Such targets can represent economically or technically desirable steady-state values._{yc}An alternative measure is to set

*w*> 0 for at least_{∆u}*n*MVs to discourage the controller from changing them._{u}– n_{yc}

If

*n*<_{u}*n*, you do not have enough degrees of freedom to keep all required OVs at a setpoint. In this case, consider prioritizing reference tracking. To do so, set_{yc}`Weights.OV(:,i) > 0`

to specify the priority for the*i*th OV. Rough guidelines for this are as follows:0.05 — Low priority: Large tracking error acceptable

0.2 — Below-average priority

1 — Average priority – the default. Use this value if

*n*= 1._{yc}5 — Above average priority

20 — High priority: Small tracking error desired

By default, `Weights.MV = zeros(1,nu)`

. If some MVs have targets, the
corresponding MV reference tracking weights must be nonzero. Otherwise, the targets are
ignored. If the number of MV targets is less than
(*n _{u}* –

Otherwise, the MV and OV reference tracking goals are likely to conflict. Prioritize
by setting the `Weights.MV(:,i)`

values in a manner similar to that
suggested for `Weights.OV`

(see above). Typical practice sets the average
MV tracking priority lower than the average OV tracking priority (e.g., 0.2 <
1).

If the *i*th MV does not have a target, set
`Weights.MV(:,i)`

= 0 (the default).

By default,

`Weights.MVRate = 0.1*ones(1,nu)`

. The reasons for this default include:If the plant is open-loop stable, large increments are unnecessary and probably undesirable. For example, when model predictions are imperfect, as is always the case in practice, more conservative increments usually provide more robust controller performance, but poorer reference tracking.

These values force the QP Hessian matrix to be positive-definite, such that the QP has a unique solution if no constraints are active.

To encourage the controller to use even smaller increments for the

*i*th MV, increase the`Weights.MVRate(:,i)`

value.If the plant is open-loop unstable, you might need to decrease the average

`Weight.MVRate`

value to allow sufficiently rapid response to upsets.

See Constraint Softening for tips regarding the `Weights.ECR`

property.

To focus on tuning individual cost function weights, perform closed-loop simulation tests under the following conditions:

No constraints.

No prediction error. The controller prediction model should be identical to the plant model. Both the

**MPC Designer**app and the`sim`

function provide the option to simulate under these conditions.

Use changes in the reference and measured disturbance signals (if any) to force a dynamic response. Based on the results of each test, consider changing the magnitudes of selected weights.

One suggested approach is to use constant `Weights.OV(:,i) = 1`

to
signify “average OV tracking priority,” and adjust all other weights to be
relative to this value. Use the `sensitivity`

command for guidance. Use the
`review`

command to check for typical tuning issues, such as lack of closed-loop
stability.

See Adjust Disturbance and Noise Models for tests focusing on the disturbance rejection ability of the controller.

Once you have weights that work well under the above conditions, check for sensitivity to prediction error. There are several ways to do so:

If you have a nonlinear plant model of your system, such as a Simulink

^{®}model, simulate the closed-loop performance at operating points other than that for which the LTI prediction model applies.Alternatively, run closed-loop simulations in which the LTI model representing the plant differs (such as in structure or parameter values) from that used at the MPC prediction model. Both the

**MPC Designer**app and the`sim`

function provide the option to simulate under these conditions. For an example, see Test Controller Robustness.

If controller performance seems to degrade significantly in comparison to tests with no prediction error, for an open-loop stable plant, consider making the controller less aggressive.

In **MPC Designer**, on the **Tuning** tab, you can do so using
the **Closed-Loop Performance** slider.

Moving towards more robust control decreases OV/MV weights and increases MV Rate weights, which leads to relaxed control of outputs and more conservative control moves.

At the command line, you can make the following changes to decrease controller aggressiveness:

Increase all

`Weight.MVRate`

values by a multiplicative factor of order 2.Decrease all

`Weight.OV`

and`Weight.MV`

values by dividing by the same factor.

After adjusting the weights, reevaluate performance both with and without prediction error.

If both are now acceptable, stop tuning the weights.

If there is improvement but still too much degradation with model error, increase the controller robustness further.

If the change does not noticeably improve performance, restore the original weights and focus on state estimator tuning (see Adjust Disturbance and Noise Models).

Finally, if tuning changes do not provide adequate robustness, consider one of the following options: