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 N_Y elements, where N_y 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 N_y 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.

Use the switch input port to select the active controller. The switch input signal must be a scalar integer from 1 to N_c, where N_c is the number of specified candidate controllers. At each control instant, this signal designates the active controller. A switch value of 1 corresponds to the first entry in the cell array of candidate controllers, a value of 2 corresponds to the second controller, and so on.

If the switch signal is outside of the range 1 to N_c, the block retains the previous controller output.

Measured output signals, specified as a vector signal. The candidate controllers use the measured plant outputs to improve their state estimates.

All candidate controllers must use the same state estimation option, either default or custom. If your candidate controllers use default state estimation, you must connect the measured plant outputs to the mo input port. If your candidate controllers use 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 candidate controllers use the connected state estimates instead of estimating the states using its built-in estimator. Use custom state estimates when an alternative estimation technique is considered superior to the built-in estimator or when the states are fully measurable.

All candidate controllers must use the same state estimation option, either default or custom. If your candidate controllers use custom state estimation, you must connect current state estimates to the x[k|k] input port. If your candidate controllers use default state estimation, you must connect a signal to the mo input port.

When you use custom state estimation, all candidate controllers must have the same dimensions. All candidate controllers must use the same state definitions (number and order of states) for their respective plant, disturbance and measurement noise models.

Dependencies

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

Minimum output variable constraints, specified as a row vector that contains N_y finite values, where N_y 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.

Dependencies

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

Maximum output variable constraints, specified as a row vector that contains N_y finite values, where N_y 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.

Dependencies

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

Minimum manipulated variable constraints, specified as a row vector that contains N_mv finite values, where N_mv 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.

Dependencies

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

Maximum manipulated variable constraints, specified as a row vector that contains N_mv finite values, where N_mv 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.

Dependencies

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

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 N_y elements, where N_y 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 N_y 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 N_mv elements, where N_mv 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 N_mv 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 N_mv elements, where N_mv 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 N_mv 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.

Optimal manipulated variable control action, output as a column vector signal of length N_mv, where N_mv is the number of manipulated variables. The Multiple MPC Controllers block passes the output of the active controller to the mv output port.

If the solver of the active controller 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 active controller is:

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 of the active controller, output as an integer signal.

If the active 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:

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 of the active controller, 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 manipulated variable sequence, returned as a matrix signal with p+1 rows and N_mv columns, where p is the prediction horizon and N_mv 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 N_x columns, where p is the prediction horizon and N_x 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 N_y columns, where p is the prediction horizon and N_y 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.

Candidate controllers, specified as one of the following:

The specified array must contain at least two candidate controllers. The first entry in the cell array is the controller that corresponds to a switch input value of 1, the second corresponds to a switch input value of 2, and so on.

Programmatic Use

Initial states for the candidate controllers, specified as one of the following:

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 objects.

If custom state estimation is enabled, the block ignores Cell Array of Initial Controller States parameter.

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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

Programmatic Use

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.

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