MPC Controller

Simulate model predictive controller

  • Library:
  • Model Predictive Control Toolbox

Description

The MPC Controller block receives the current measured output signal (mo), reference signal (ref), and optional measured disturbance signal (md). The block computes the optimal manipulated variable (mv) by solving a quadratic programming problem using either the default KWIK solver or a custom QP solver. For more information, see QP Solver.

To use the block in simulation and code generation, you must specify an mpc object, which defines a model predictive controller. This controller must have already been designed for the plant that it controls.

Because the MPC Controller block uses MATLAB Function blocks, it requires compilation each time you change the MPC object and block. Also, because MATLAB® does not allow compiled code to reside in any MATLAB product folder, you must use a non-MATLAB folder to work on your Simulink® model when you use MPC blocks.

Ports

Input

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Required Inputs

Plant output reference values, specified as a row vector signal or matrix signal.

To use the same reference values across the prediction horizon, connect ref to a row vector signal with NY elements, where Ny is the number of output variables. Each element specifies the reference for an output variable.

To vary the references over the prediction horizon (previewing) from time k+1 to time k+p, connect ref to a matrix signal with Ny columns and up to p rows. Here, k is the current time and p is the prediction horizon. Each row contains the references for one prediction horizon step. If you specify fewer than p rows, the final references are used for the remaining steps of the prediction horizon.

Measured output signals, specified as a vector signal. The MPC controller uses the measured plant outputs to improve its state estimates. If your controller uses default state estimation, you must connect the measured plant outputs to the mo input port. If your controller uses custom state estimation, you must connect a signal to the x[k|k] input port.

Dependencies

To enable this port, clear the Use custom state estimation instead of using the built-in Kalman filter parameter.

Custom state estimate, specified as a vector signal. The MPC controller uses the connected state estimates instead of estimating the states using its build-in estimator. If your controller uses custom state estimation, you must connect current state estimates to the x[k|k] input port. If your controller uses default state estimation, you must connect a signal to the mo input port.

Even though measurement noise states (if any) are not used in MPC optimization, the custom state vector must contain all the states defined in the mpcstate object of the controller, including the plant, disturbance, and noise model states.

Use custom state estimates when an alternative estimation technique is considered superior to the built-in estimator or when the states are fully measurable.

Dependencies

To enable this port, select the Use custom state estimation instead of using the built-in Kalman filter parameter.

Additional Inputs

If your controller model has measured disturbances, enable this input port, and connect a row vector or matrix signal. If your controller has measured disturbances, you must enable this port.

To use the same measured disturbance values across the prediction horizon, connect md to a row vector signal with Nmd elements, where Nmd is the number of manipulated variables. Each element specifies the value for a measured disturbance.

To vary the disturbances over the prediction horizon (previewing) from time k to time k+p, connect md to a matrix signal with Nmd columns and up to p+1 rows. Here, k is the current time and p is the prediction horizon. Each row contains the disturbances for one prediction horizon step. If you specify fewer than p+1 rows, the final disturbances are used for the remaining steps of the prediction horizon.

Control signals used in plant at previous control interval, specified as a vector signal of lengthNmv, where Nmv is the number of manipulated variables. Use this input port when:

  • You know your controller is not always in control of the plant

  • When the actual MV signals applied to the plant can potentially differ from the values generated by the controller, such as control signal saturation

Controller state estimation assumes that the MV is piecewise constant. Therefore, at time tk, the ext.mv value must be the effective MV between times tk–1 and tk. For example, if the MV is actually varying over this interval, you might supply the time-averaged value evaluated at time tk.

Note

  • Connect ext.mv to the MV signals actually applied to the plant in the previous control interval. Typically, these MV signals are the values generated by the controller, though this is not always the case. For example, if your controller is offline and running in tracking mode; that is, the controller output is not driving the plant, then feeding the actual control signal to ext.mv can help achieve bumpless transfer when the controller is switched back online.

  • Using this option can cause an algebraic loop in the Simulink model, since there is direct feedthrough from the ext.mv inport to the mv outport. To prevent such algebraic loops, insert a Memory block or Unit Delay block.

For an example that uses the external manipulated variable input port for bumpless transfer, see Switch Controller Online and Offline with Bumpless Transfer.

Dependencies

To enable this port, select the External manipulated variable parameter.

To turn off the controller optimization calculations, connect switch to a nonzero signal.

Disabling optimization calculations reduces computational effort when the controller output is not needed, such as when the system is operating manually or another controller has taken over. However, the controller continues to update its internal state estimates in the usual way. Therefore, it is ready to resume optimization calculations whenever the switch signal returns to zero. While controller optimization is off, the block passes the current ext.mv signal to the controller output. If the ext.mv inport is not enabled, the controller output is held at the value it had when optimization was disabled.

For an example that uses the external manipulated variable input port for bumpless transfer, see Switch Controller Online and Offline with Bumpless Transfer.

Dependencies

To enable this port, select the Use external signal to enable or disable optimization parameter.

To specify manipulated variable targets, enable this input port, and connect a row vector or matrix signal. To make a given manipulated variable track its specified target value, you must also specify a nonzero tuning weight for that manipulated variable.

To use the same manipulated variable targets across the prediction horizon, connect mv.target to a row vector signal with Nmv elements, where Nmv is the number of manipulated variables. Each element specifies the target for a manipulated variable.

To vary the targets over the prediction horizon (previewing) from time k to time k+p-1, connect mv.target to a matrix signal with Nmv columns and up to p rows. Here, k is the current time and p is the prediction horizon. Each row contains the targets for one prediction horizon step. If you specify fewer than p rows, the final targets are used for the remaining steps of the prediction horizon.

Dependencies

To enable this port, select the Targets for manipulated variables parameter.

Online Constraints

Minimum output variable constraints, specified as a row vector that contains Ny finite values, where Ny is the number of outputs. Each element specifies the lower bound for an output variable. The ith element of ymin replaces the OutputVariables(i).Min property of the controller at run time.

If an output variable does not have a lower bound specified in the controller object, then the corresponding connected signal value is ignored.

If this parameter is not selected, the block uses the constant constraint values stored within its mpc object.

Note

You cannot specify time-varying constraints at run time using a matrix signal.

If the OutputVariables(i).Min property of the controller is specified as a vector (that is, the constraint varies over the prediction horizon), the ith element of ymin replaces the first finite entry in this vector, and the remaining values shift to retain the same constraint profile.

Dependencies

To enable this port, select the Lower OV limits parameter.

Maximum output variable constraints, specified as a row vector that contains Ny finite values, where Ny is the number of outputs. Each element specifies the upper bound for an output variable. The ith element of ymax replaces the OutputVariables(i).Max property of the controller at run time.

If an output variable does not have an upper bound specified in the controller object, then the corresponding connected signal value is ignored.

If this parameter is not selected, the block uses the constant constraint values stored within its mpc object.

Note

You cannot specify time-varying constraints at run time using a matrix signal.

If the OutputVariables(i).Max property of the controller is specified as a vector (that is, the constraint varies over the prediction horizon), the ith element of ymax replaces the first finite entry in this vector, and the remaining values shift to retain the same constraint profile.

Dependencies

To enable this port, select the Upper OV limits parameter.

Minimum manipulated variable constraints, specified as a row vector that contains Nmv finite values, where Nmv is the number of manipulated variables. Each element specifies the lower bound for a manipulated variable. The ith element of umin replaces the ManipulatedVariables(i).Min property of the controller at run time.

If a manipulated variable does not have a lower bound specified in the controller object, then the corresponding connected signal value is ignored.

If this parameter is not selected, the block uses the constant constraint values stored within its mpc object.

Note

You cannot specify time-varying constraints at run time using a matrix signal.

If the ManipulatedVariables(i).Min property of the controller is specified as a vector (that is, the constraint varies over the prediction horizon), the ith element of umin replaces the first finite entry in this vector, and the remaining values shift to retain the same constraint profile.

Dependencies

To enable this port, select the Lower MV limits parameter.

Maximum manipulated variable constraints, specified as a row vector that contains Nmv finite values, where Nmv is the number of manipulated variables. Each element specifies the upper bound for a manipulated variable. The ith element of umax replaces the ManipulatedVariables(i).Max property of the controller at run time.

If a manipulated variable does not have an upper bound specified in the controller object, then the corresponding connected signal value is ignored.

If this parameter is not selected, the block uses the constant constraint values stored within its mpc object.

Note

You cannot specify time-varying constraints at run time using a matrix signal.

If the ManipulatedVariables(i).Max property of the controller is specified as a vector (that is, the constraint varies over the prediction horizon), the ith element of umax replaces the first finite entry in this vector, and the remaining values shift to retain the same constraint profile.

Dependencies

To enable this port, select the Upper MV limits parameter.

Manipulated variable constraint matrix, specified as an Nc-by-Nmv matrix signal, where Nc is the number of mixed input/output constraints and Nmv is the number of manipulated variables.

If you define E in the mpc object, you must connect a signal to the E input port. Otherwise, connect a zero matrix with the correct size.

To specify run-time mixed input/output constraints, use the E input port along with the F, G, and S ports. These constraints replace the mixed input/output constraints previously set using setconstraint. For more information on mixed input/output constraints, see Constraints on Linear Combinations of Inputs and Outputs.

The number of mixed input/output constraints cannot change at run time. Therefore, Nc must match the number of rows in the E matrix you specified using setconstraint.

Dependencies

To enable this port, select the Custom constraints parameter.

Controlled output constraint matrix, specified as an Nc-by-Ny matrix signal, where Nc is the number of mixed input/output constraints and Ny is the number of plant outputs. If you define F in the mpc object, you must connect a signal to the F input port with same number of rows. Otherwise, connect a zero matrix with the correct size.

To specify run-time mixed input/output constraints, use the F input port along with the E, G, and S ports. These constraints replace the mixed input/output constraints previously set using setconstraint. For more information on mixed input/output constraints, see Constraints on Linear Combinations of Inputs and Outputs.

The number of mixed input/output constraints cannot change at run time. Therefore, Nc must match the number of rows in the F matrix you specified using setconstraint.

Dependencies

To enable this port, select the Custom constraints parameter.

Custom constraint vector, specified as a row vector signal of length Nc, where Nc is the number of mixed input/output constraints. If you define G in the mpc object, you must connect a signal to the G input port with same number of rows. Otherwise, connect a zero matrix with the correct size.

To specify run-time mixed input/output constraints, use the G input port along with the E, F, and S ports. These constraints replace the mixed input/output constraints previously set using setconstraint. For more information on mixed input/output constraints, see Constraints on Linear Combinations of Inputs and Outputs.

The number of mixed input/output constraints cannot change at run time. Therefore, Nc must match the number of rows in the G matrix you specified using setconstraint.

Dependencies

To enable this port, select the Custom constraints parameter.

Measured disturbance constraint matrix, specified as an Nc-by-nN matrix signal, where Nc is the number of mixed input/output constraints, and Nv is the number of measured disturbances. If you define S in the mpc object, you must connect a signal to the S input port with same number of rows. Otherwise, connect a zero matrix with the correct size.

To specify run-time mixed input/output constraints, use the S input port along with the E, F, and G ports. These constraints replace the mixed input/output constraints previously set using setconstraint. For more information on mixed input/output constraints, see Constraints on Linear Combinations of Inputs and Outputs.

The number of mixed input/output constraints cannot change at run time. Therefore, Nc must match the number of rows in the G matrix you specified using setconstraint.

Dependencies

To enable this port, select the Custom constraints parameter. This port is added only if the mpc object has measured disturbances.

Online Tuning Weights

To specify run-time output variable tuning weights, enable this input port. If this port is disabled, the block uses the tuning weights specified in the Weights.OutputVariables property of its controller object. These tuning weights penalize deviations from output references.

If the MPC controller object uses constant output tuning weights over the prediction horizon, you can specify only constant output tuning weights at runtime. Similarly, if the MPC controller object uses output tuning weights that vary over the prediction horizon, you can specify only time-varying output tuning weights at runtime

To use constant tuning weights over the prediction horizon, connect y.wt to a row vector signal with Ny elements, where Ny is the number of outputs. Each element specifies a nonnegative tuning weight for an output variable. For more information on specifying tuning weights, see Tune Weights.

To vary the tuning weights over the prediction horizon from time k+1 to time k+p, connect y.wt to a matrix signal with Ny columns and up to p rows. Here, k is the current time and p is the prediction horizon. Each row contains the tuning weights for one prediction horizon step. If you specify fewer than p rows, the tuning weights in the final row apply for the remainder of the prediction horizon. For more information on varying weights over the prediction horizon, see Time-Varying Weights and Constraints.

Dependencies

To enable this port, select the OV weights parameter.

To specify run-time manipulated variable tuning weights, enable this input port. If this port is disabled, the block uses the tuning weights specified in the Weights.ManipulatedVariables property of its controller object. These tuning weights penalize deviations from MV targets.

If the MPC controller object uses constant manipulated variable tuning weights over the prediction horizon, you can specify only constant manipulated variable tuning weights at runtime. Similarly, if the MPC controller object uses manipulated variable tuning weights that vary over the prediction horizon, you can specify only time-varying manipulated variable tuning weights at runtime

To use the same tuning weights over the prediction horizon, connect u.wt to a row vector signal with Nmv elements, where Nmv is the number of manipulated variables. Each element specifies a nonnegative tuning weight for a manipulated variable. For more information on specifying tuning weights, see Tune Weights.

To vary the tuning weights over the prediction horizon from time k to time k+p-1, connect u.wt to a matrix signal with Nmv columns and up to p rows. Here, k is the current time and p is the prediction horizon. Each row contains the tuning weights for one prediction horizon step. If you specify fewer than p rows, the tuning weights in the final row apply for the remainder of the prediction horizon. For more information on varying weights over the prediction horizon, see Time-Varying Weights and Constraints.

Dependencies

To enable this port, select the MV weights parameter.

To specify run-time manipulated variable rate tuning weights, enable this input port. If this port is disabled, the block uses the tuning weights specified in the Weights.ManipulatedVariablesRate property of its controller object. These tuning weights penalize large changes in control moves.

If the MPC controller object uses constant manipulated variable rate tuning weights over the prediction horizon, you can specify only constant manipulated variable tuning rate weights at runtime. Similarly, if the MPC controller object uses manipulated variable rate tuning weights that vary over the prediction horizon, you can specify only time-varying manipulated variable rate tuning weights at runtime

To use the same tuning weights over the prediction horizon, connect du.wt to a row vector signal with Nmv elements, where Nmv is the number of manipulated variables. Each element specifies a nonnegative tuning weight for a manipulated variable rate. For more information on specifying tuning weights, see Tune Weights.

To vary the tuning weights over the prediction horizon from time k to time k+p-1, connect du.wt to a matrix signal with Nmv columns and up to p rows. Here, k is the current time and p is the prediction horizon. Each row contains the tuning weights for one prediction horizon step. If you specify fewer than p rows, the tuning weights in the final row apply for the remainder of the prediction horizon. For more information on varying weights over the prediction horizon, see Time-Varying Weights and Constraints.

Dependencies

To enable this port, select the MVRate weights parameter.

To specify a run-time slack variable tuning weight, enable this input port and connect a scalar signal. If this port is disabled, the block uses the tuning weight specified in the Weights.ECR property of its controller object.

The slack variable tuning weight has no effect unless your controller object defines soft constraints whose associated ECR values are nonzero. If there are soft constraints, increasing the ecr.wt value makes these constraints relatively harder. The controller then places a higher priority on minimizing the magnitude of the predicted worst-case constraint violation.

Dependencies

To enable this port, select the ECR weight parameter.

Online Horizons

Prediction horizon, specified as positive integer signal. The prediction horizon signal value must be less than or equal to the Maximum prediction horizon parameter.

At run time, the values of p overrides the default prediction horizon specified in the controller object. For more information, see Adjust Horizons at Run Time.

Dependencies

To enable this port, select the Adjust prediction horizon and control horizon at run time parameter.

Control horizon, specified as one of the following:

  • Positive integer signal less than or equal to the prediction horizon.

  • Vector signal of positive integers specifying blocking interval lengths. For more information, see Manipulated Variable Blocking.

At run time, the values of m overrides the default control horizon specified in the controller object. For more information, see Adjust Horizons at Run Time.

Dependencies

To enable this port, select the Adjust prediction horizon and control horizon at run time parameter.

Output

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Required Output

Optimal manipulated variable control action, output as a column vector signal of length Nmv, where Nmv is the number of manipulated variables.

If the solver converges to a local optimum solution (qp.status is positive), then mv contains the optimal solution.

If the solver fails (qp.status is negative), then mv remains at its most recent successful solution; that is, the controller output freezes.

If the solver reaches the maximum number of iterations without finding an optimal solution (qp.status is zero) and the Optimization.UseSuboptimalSolution property of the controller is:

  • true, then mv contains the suboptimal solution

  • false, then mv then mv remains at its most recent successful solution

Additional Outputs

Objective function cost, output as a nonnegative scalar signal. The cost quantifies the degree to which the controller has achieved its objectives. The cost value is calculated using the scaled MPC cost function in which every term is offset-free and dimensionless.

The cost value is only meaningful when the qp.status output is nonnegative.

Dependencies

To enable this port, select the Optimal cost parameter.

Optimization status, output as an integer signal.

If the controller solves the QP problem for a given control interval, the qp.status output returns the number of QP solver iterations used in computation. This value is a finite, positive integer and is proportional to the time required for the calculations. Therefore, a large value means a relatively slow block execution for this time interval.

The QP solver can fail to find an optimal solution for the following reasons:

  • qp.status = 0 — The QP solver cannot find a solution within the maximum number of iterations specified in the mpc object. In this case, if the Optimizer.UseSuboptimalSolution property of the controller is false, the block holds its mv output at the most recent successful solution. Otherwise, it uses the suboptimal solution found during the last solver iteration.

  • qp.status = -1 — The QP solver detects an infeasible QP problem. See Monitoring Optimization Status to Detect Controller Failures for an example where a large, sustained disturbance drives the output variable outside its specified bounds. In this case, the block holds its mv output at the most recent successful solution.

  • qp.status = -2 — The QP solver has encountered numerical difficulties in solving a severely ill-conditioned QP problem. In this case, the block holds its mv output at the most recent successful solution.

In a real-time application, you can use qp.status to set an alarm or take other special action.

Dependencies

To enable this port, select the Optimization status parameter.

Estimated controller states at each control instant, output as a vector signal. The estimated states include the plant, disturbance, and noise model states.

Dependencies

To enable this port, select the Estimated controller states parameter.

Optimal Sequences

Optimal manipulated variable sequence, returned as a matrix signal with p+1 rows and Nmv columns, where p is the prediction horizon and Nmv is the number of manipulated variables.

The first p rows of mv.seq contain the calculated optimal manipulated variable values from current time k to time k+p-1. The first row of mv.seq contains the current manipulated variable values (output mv). Since the controller does not calculate optimal control moves at time k+p, the final two rows of mv.seq are identical.

Dependencies

To enable this port, select the Optimal control sequence parameter.

Optimal prediction model state sequence, returned as a matrix signal with p+1 rows and Nx columns, where p is the prediction horizon and Nx is the number of states.

The first p rows of x.seq contain the calculated optimal state values from current time k to time k+p-1. The first row of x.seq contains the current estimated state values. Since the controller does not calculate optimal states at time k+p, the final two rows of x.seq are identical.

Dependencies

To enable this port, select the Optimal state sequence parameter.

Optimal output variable sequence, returned as a matrix signal with p+1 rows and Ny columns, where p is the prediction horizon and Ny is the number of output variables.

The first p rows of y.seq contain the calculated optimal output values from current time k to time k+p-1. The first row of y.seq is computed based on the current estimated states and the current measured disturbances (first row of input md). Since the controller does not calculate optimal output values at time k+p, the final two rows of y.seq are identical.

Dependencies

To enable this port, select the Optimal output sequence parameter.

Parameters

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Specify an mpc object that defines an implicit MPC controller by entering the name of an mpc object from the MATLAB workspace.

Programmatic Use

Block Parameter: mpcobj
Type: string, character vector
Default: ""

Specify the initial controller state. If you leave this parameter blank, the block uses the nominal values defined in the Model.Nominal property of the mpc object. To override the default, create an mpcstate object in your workspace, and enter its name in the field.

Use this parameter make the controller states reflect the true plant environment at the start of your simulation to the best of your knowledge. This initial states can differ from the nominal states defined in the mpc object.

If custom state estimation is enabled, the block ignores Initial Controller State parameter.

Programmatic Use

Block Parameter: x0
Type: string, character vector
Default: ""

To interactively modify the controller specified using the MPC Controller parameter, open the MPC Designer app by clicking Design. For example, you can:

  • Import a new prediction model.

  • Change horizons, constraints, and weights.

  • Evaluate MPC performance with a linear plant.

  • Export the updated controller to the MATLAB workspace.

If you have an existing mpc object in the MATLAB workspace, specify the name of that object using the MPC Controller parameter.

If you do not have an existing mpc object in the MATLAB workspace, leave the MPC Controller parameter empty. With the MPC Controller block connected to the plant, open MPC Designer by clicking Design. Using the app, linearize the Simulink model at a specified operating point, and design your controller. To use this design approach, you must have Simulink Control Design™ software. For more information, see Design MPC Controller in Simulink and Linearize Simulink Models Using MPC Designer.

Once you specify a controller using the MPC Controller parameter, you can review your design for run-time stability and robustness issues by clicking Review. For more information, see Review Model Predictive Controller for Stability and Robustness Issues.

General Tab

If your controller has measured disturbances, you must select this parameter to add the md output port to the block.

Programmatic Use

Block Parameter: md_inport
Type: string, character vector
Values: "off", "on"
Default: "on"

Select this parameter to add the ext.mv input port to the block.

Programmatic Use

Block Parameter: mv_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the mv.target input port to the block.

Programmatic Use

Block Parameter: uref_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the cost output port to the block.

Programmatic Use

Block Parameter: return_cost
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the qp.status output port to the block.

Programmatic Use

Block Parameter: return_qpstatus
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the est.state output port to the block.

Programmatic Use

Block Parameter: return_state
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the mv.seq output port to the block.

Programmatic Use

Block Parameter: return_mvseq
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the x.seq output port to the block.

Programmatic Use

Block Parameter: return_xseq
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the y.seq output port to the block.

Programmatic Use

Block Parameter: return_ovseq
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to remove the mo input port and add the x[k|k] input port.

Programmatic Use

Block Parameter: state_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Online Features Tab

Select this parameter to add the ymin input port to the block.

Programmatic Use

Block Parameter: ymin_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the ymax input port to the block.

Programmatic Use

Block Parameter: ymax_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the umin input port to the block.

Programmatic Use

Block Parameter: umin_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the umax input port to the block.

Programmatic Use

Block Parameter: umax_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the E, F, G, and S input ports to the block.

Programmatic Use

Block Parameter: cc_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the y.wt input port to the block.

Programmatic Use

Block Parameter: ywt_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the u.wt input port to the block.

Programmatic Use

Block Parameter: uwt_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the du.wt input port to the block.

Programmatic Use

Block Parameter: duwt_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the ecr.wt input port to the block.

Programmatic Use

Block Parameter: rhoeps_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the p and m input port to the block.

Programmatic Use

Block Parameter: pm_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the p and m input port to the block.

Dependencies

To enable this parameter, select the Adjust prediction horizon and control horizon at run time parameter.

Programmatic Use

Block Parameter: MaximumP
Type: string, character vector
Default: "10"

Default Conditions Tab

Default block sample time for performing simulation, trimming, or linearization using the MPC Designer app. You must specify a sample time that is compatible with your Simulink model design.

Dependencies

This parameter applies only when the MPC Controller parameter is empty and you open MPC Designer using the Design button.

Programmatic Use

Block Parameter: n_ts
Type: string, character vector
Default: "1"

Default prediction horizon for performing simulation, trimming, or linearization using the MPC Designer app. You must specify a prediction horizon that is compatible with your Simulink model design.

Dependencies

This parameter applies only when the MPC Controller parameter is empty and you open MPC Designer using the Design button.

Programmatic Use

Block Parameter: n_p
Type: string, character vector
Default: "10"

Default number of manipulated variables for performing simulation, trimming, or linearization using the MPC Designer app. You must specify a value that is compatible with your Simulink model design.

Dependencies

This parameter applies only when the MPC Controller parameter is empty and you open MPC Designer using the Design button.

Programmatic Use

Block Parameter: n_mv
Type: string, character vector
Default: "1"

Default number of measured disturbances for performing simulation, trimming, or linearization using the MPC Designer app. You must specify a value that is compatible with your Simulink model design.

Dependencies

  • This parameter applies only when the MPC Controller parameter is empty and you open MPC Designer using the Design button.

  • To use this parameter, you must select the Measured disturbance parameter.

Programmatic Use

Block Parameter: n_md
Type: string, character vector
Default: "1"

Default number of unmeasured disturbances for performing simulation, trimming, or linearization using the MPC Designer app. You must specify a value that is compatible with your Simulink model design.

Dependencies

This parameter applies only when the MPC Controller parameter is empty and you open MPC Designer using the Design button.

Programmatic Use

Block Parameter: n_ud
Type: string, character vector
Default: "0"

Default number of measured outputs for performing simulation, trimming, or linearization using the MPC Designer app. You must specify a value that is compatible with your Simulink model design.

Dependencies

This parameter applies only when the MPC Controller parameter is empty and you open MPC Designer using the Design button.

Programmatic Use

Block Parameter: n_mo
Type: string, character vector
Default: "1"

Default number of unmeasured outputs for performing simulation, trimming, or linearization using the MPC Designer app. You must specify a value that is compatible with your Simulink model design.

Dependencies

This parameter applies only when the MPC Controller parameter is empty and you open MPC Designer using the Design button.

Programmatic Use

Block Parameter: n_uo
Type: string, character vector
Default: "0"

Others Tab

Select this parameter to inherit the sample time of the parent subsystem as the block sample time. Doing so allows you to conditionally execute this block inside Function-Call Subsystem or Triggered Subsystem blocks. For an example, see Using MPC Controller Block Inside Function-Call and Triggered Subsystems.

Note

You must execute Function-Call Subsystem or Triggered Subsystem blocks at the sample rate of the controller. Otherwise, you can see unexpected results.

To view the sample time of a block, in the Simulink Editor, select Display > Sample Time. Select Colors, Annotations, or All. For more information, see View Sample Time Information (Simulink).

Programmatic Use

Block Parameter: SampleTimeInherited
Type: string, character vector
Values: "off", "on"
Default: "off"

Select this parameter to add the switch input port to the block.

Programmatic Use

Block Parameter: switch_inport
Type: string, character vector
Values: "off", "on"
Default: "off"

Compatibility Considerations

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Behavior changed in R2018b

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

PLC Code Generation
Generate Structured Text code using Simulink® PLC Coder™.

Introduced before R2006a