# Nonlinear MPC Controller

Simulate nonlinear model predictive controllers

**Library:**Model Predictive Control Toolbox

## Description

The Nonlinear MPC Controller block simulates a nonlinear model predictive controller. At each control interval, the block computes optimal control moves by solving a nonlinear programming problem. For more information on nonlinear MPC, see Nonlinear MPC.

To use this block, you must first create an `nlmpc`

object in the
MATLAB^{®} workspace.

## Limitations

None of the Nonlinear MPC Controller block parameters are tunable.

## Ports

### Input

**Required Inputs**

`x`

— input

vector

Current prediction model states, specified as a vector signal of length
*N _{x}*, where

*N*is the number of prediction model states. Since the nonlinear MPC controller does not perform state estimation, you must either measure or estimate the current prediction model states at each control interval.

_{x}`ref`

— Model output reference values

row vector | matrix

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*is the number of output variables. Each element specifies the reference for an output variable.

_{y}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.

`last_mv`

— Control signals used in plant at previous control interval

vector

Control signals used in plant at previous control interval, specified as a vector
signal of length*N _{mv}*, where

*N*is the number of manipulated variables.

_{mv}**Note**

Connect **last_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
**last_mv** can help achieve bumpless transfer when the
controller is switched back online.

**Additional Inputs**

`md`

— input

row vector | matrix

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

To use the same measured disturbance values across the prediction horizon, connect **md** to a row vector signal with *N _{md}* elements, where

*N*is the number of manipulated variables. Each element specifies the value for a measured disturbance.

_{md}To vary the disturbances over the prediction horizon (previewing) from time
*k* to time *k*+*p*, connect
**md** to a matrix signal with
*N _{md}* 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.

#### Dependencies

To enable this port, select the **Measured disturbances**
parameter.

`params`

— Optional parameters

bus

If your controller uses optional parameters in its prediction model, custom cost
function, or custom constraint functions, enable this input port, and connect a
parameter bus signal with *N _{p}* elements, where

*N*is the number of parameters. For more information on creating a parameter bus signal, see

_{p}`createParameterBus`

. The controller, passes these parameters to its model
functions, cost function, constraint functions, and Jacobian functions.If your controller does not use optional parameters, you must disable
**params**.

#### Dependencies

To enable this port, select the **Model parameters**
parameter.

`mv.target`

— Manipulated variable targets

row vector | array

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 *N _{mv}* elements, where

*N*is the number of manipulated variables. Each element specifies the target for a manipulated variable.

_{mv}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 *N _{mv}* 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**

`y.min`

— Minimum output variable constraints

vector | matrix

To specify run-time minimum output variable constraints, enable this input port.
If this port is disabled, the block uses the lower bounds specified in the
`OutputVariables.Min`

property of its controller object.

To use the same bounds over the prediction horizon, connect
**y.min** to a row vector signal with
*N _{y}* elements, where

*N*is the number of outputs. Each element specifies the lower bound for an output variable.

_{y}To vary the bounds over the prediction horizon from time *k*+1 to
time *k*+*p*, connect **y.min** 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 bounds for one prediction horizon step. If you specify fewer than

*p*rows, the bounds in the final row apply for the remainder of the prediction horizon.

#### Dependencies

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

`y.max`

— Maximum output variable constraints

vector | matrix

To specify run-time maximum output variable constraints, enable this input port.
If this port is disabled, the block uses the upper bounds specified in the
`OutputVariables.Min`

property of its controller object.

To use the same bounds over the prediction horizon, connect
**y.max** to a row vector signal with
*N _{y}* elements, where

*N*is the number of outputs. Each element specifies the upper bound for an output variable.

_{y}To vary the bounds over the prediction horizon from time *k*+1 to
time *k*+*p*, connect **y.max** 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 bounds for one prediction horizon step. If you specify fewer than

*p*rows, the bounds in the final row apply for the remainder of the prediction horizon.

#### Dependencies

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

`mv.min`

— Minimum manipulated variable constraints

vector | matrix

To specify run-time minimum manipulated variable constraints, enable this input
port. If this port is disabled, the block uses the lower bounds specified in the
`ManipulatedVariables.Min`

property of its controller
object.

To use the same bounds over the prediction horizon, connect
**mv.min** to a row vector signal with
*N _{mv}* elements, where

*N*is the number of outputs. Each element specifies the lower bound for a manipulated variable.

_{mv}To vary the bounds over the prediction horizon from time *k* to
time *k*+*p*-1, connect **mv.min**
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 bounds for one prediction horizon step. If you specify fewer than

*p*rows, the bounds in the final row apply for the remainder of the prediction horizon.

#### Dependencies

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

`mv.max`

— Maximum manipulated variable constraints

vector | matrix

To specify run-time maximum manipulated variable constraints, enable this input
port. If this port is disabled, the block uses the upper bounds specified in the
`ManipulatedVariables.Max`

property of its controller
object.

To use the same bounds over the prediction horizon, connect
**mv.max** to a row vector signal with
*N _{mv}* elements, where

*N*is the number of outputs. Each element specifies the upper bound for a manipulated variable.

_{mv}To vary the bounds over the prediction horizon from time *k* to
time *k*+*p*-1, connect **mv.max**
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 bounds for one prediction horizon step. If you specify fewer than

*p*rows, the bounds in the final row apply for the remainder of the prediction horizon.

#### Dependencies

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

`dmv.min`

— Minimum manipulated variable rate constraints

vector | matrix

To specify run-time minimum manipulated variable rate constraints, enable this
input port. If this port is disabled, the block uses the lower bounds specified in the
`ManipulatedVariable.RateMin`

property of its controller object.
**dmv.min** bounds must be nonpositive.

To use the same bounds over the prediction horizon, connect
**dmv.min** to a row vector signal with
*N _{mv}* elements, where

*N*is the number of outputs. Each element specifies the lower bound for a manipulated variable rate of change.

_{mv}To vary the bounds over the prediction horizon from time *k* to
time *k*+*p*-1, connect **dmv.min**
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 bounds for one prediction horizon step. If you specify fewer than

*p*rows, the bounds in the final row apply for the remainder of the prediction horizon.

#### Dependencies

To enable this port, select the **Lower MVRate limits**
parameter.

`dmv.max`

— Maximum manipulated variable rate constraints

vector | matrix

To specify run-time maximum manipulated variable rate constraints, enable this
input port. If this port is disabled, the block uses the upper bounds specified in the
`ManipulatedVariables.RateMax`

property of its controller object.
**dmv.max** bounds must be nonnegative.

To use the same bounds over the prediction horizon, connect
**dmv.max** to a row vector signal with
*N _{mv}* elements, where

*N*is the number of outputs. Each element specifies the upper bound for a manipulated variable rate of change.

_{mv}To vary the bounds over the prediction horizon from time *k* to
time *k*+*p*-1, connect **dmv.max**
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 bounds for one prediction horizon step. If you specify fewer than

*p*rows, the bounds in the final row apply for the remainder of the prediction horizon.

#### Dependencies

To enable this port, select the **Upper MVRate limits**
parameter.

`x.min`

— Minimum state constraints

vector | matrix

To specify run-time minimum state constraints, enable this input port. If this
port is disabled, the block uses the lower bounds specified in the
`States.Min`

property of its controller object.

To use the same bounds over the prediction horizon, connect
**x.min** to a row vector signal with
*N _{x}* elements, where

*N*is the number of outputs. Each element specifies the lower bound for a state.

_{x}To vary the bounds over the prediction horizon from time *k*+1 to
time *k*+*p*, connect **x.min** 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 bounds for one prediction horizon step. If you specify fewer than

*p*rows, the bounds in the final row apply for the remainder of the prediction horizon.

#### Dependencies

To enable this port, select the **Lower state limits**
parameter.

`x.max`

— Maximum state constraints

vector | matrix

To specify run-time maximum state constraints, enable this input port. If this
port is disabled, the block uses the upper bounds specified in the
`States.Max`

property of its controller object.

To use the same bounds over the prediction horizon, connect
**x.max** to a row vector signal with
*N _{x}* elements, where

*N*is the number of outputs. Each element specifies the upper bound for a state.

_{x}To vary the bounds over the prediction horizon from time *k*+1 to
time *k*+*p*, connect **x.max** 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 bounds for one prediction horizon step. If you specify fewer than

*p*rows, the bounds in the final row apply for the remainder of the prediction horizon.

#### Dependencies

To enable this port, select the **Upper state limits**
parameter.

**Online Tuning Weights**

`y.wt`

— Output variable tuning weights

row vector | matrix

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*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.

_{y}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 Setting Time-Varying Weights and Constraints with MPC Designer.

#### Dependencies

To enable this port, select the **OV weights** parameter.

`mv.wt`

— Manipulated variable tuning weights

row vector | matrix

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.

To use the same tuning weights over the prediction horizon, connect
**mv.wt** to a row vector signal with
*N _{mv}* elements, where

*N*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.

_{mv}To vary the tuning weights over the prediction horizon from time
*k* to time *k*+*p*-1, connect
**mv.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 Setting Time-Varying Weights and Constraints with MPC Designer.

#### Dependencies

To enable this port, select the **MV weights**
parameter.

`dmv.wt`

— Manipulated variable rate tuning weights

row vector | matrix

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.

To use the same tuning weights over the prediction horizon, connect
**dmv.wt** to a row vector signal with
*N _{mv}* elements, where

*N*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.

_{mv}To vary the tuning weights over the prediction horizon from time
*k* to time *k*+*p*-1, connect
**dmv.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 Setting Time-Varying Weights and Constraints with MPC Designer.

#### Dependencies

To enable this port, select the **MVRate weights**
parameter.

`ecr.wt`

— Slack variable tuning weight

scalar

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.

**Initial Guesses**

`mv.init`

— Initial guesses for the optimal manipulated variable solutions

vector | matrix

To specify initial guesses for the optimal manipulated variable solutions, enable this input port. If this port is disabled, the block uses the optimal control sequences calculated in the previous control interval as initial guesses.

To use the same initial guesses over the prediction horizon, connect
**mv.init** to a vector signal with
*N _{mv}* elements, where

*N*is the number of manipulated variables. Each element specifies the initial guess for a manipulated variable.

_{mv}To vary the initial guesses over the prediction horizon from time
*k* to time *k*+*p*-1, connect
**mv.init** 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 initial guesses for one prediction horizon step. If you specify fewer than

*p*rows, the guesses in the final row apply for the remainder of the prediction horizon.

#### Dependencies

To enable this port, select the **Initial guess**
parameter.

`x.init`

— Initial guesses for the optimal state variable solutions

vector | matrix

To specify initial guesses for the optimal state solutions, enable this input port. If this port is disabled, the block uses the optimal state sequences calculated in the previous control interval as initial guesses.

To use the same initial guesses over the prediction horizon, connect
**x.init** to a vector signal with
*N _{x}* elements, where

*N*is the number of states. Each element specifies the initial guess for a state.

_{x}To vary the initial guesses over the prediction horizon from time
*k* to time *k*+*p*-1, connect
**x.init** to a matrix signal with
*N _{x}* columns and up to

*p*rows. Here,

*k*is the current time and

*p*is the prediction horizon. Each row contains the initial guesses for one prediction horizon step. If you specify fewer than

*p*rows, the guesses in the final row apply for the remainder of the prediction horizon.

#### Dependencies

To enable this port, select the **Initial guess**
parameter.

`e.init`

— Initial guess for the slack variable at the solution

nonnegative scalar

To specify an initial guess for the slack variable at the solution, enable this
input port and connect a nonnegative scalar signal. If this port is disabled, the
block uses an initial guess of `0`

.

#### Dependencies

To enable this port, select the **Initial guess**
parameter.

### Output

**Required Output**

`mv`

— Optimal manipulated variable control action

column vector

Optimal manipulated variable control action, output as a column vector signal of
length *N _{mv}*, where

*N*is the number of manipulated variables.

_{mv}If the solver converges to a local optimum solution
(**nlp.status** is positive), then **mv** contains
the optimal solution.

If the solver reaches the maximum number of iterations without finding an optimal
solution (**nlp.status** is zero) and the
`Optimization.UseSuboptimalSolution`

property of the controller
is:

`true`

, then**mv**contains the suboptimal solution`false`

, then**mv**is the same as**last_mv**

If the solver fails (**nlp.status** is negative), then
**mv** is the same as **last_mv**.

**Additional Outputs**

`cost`

— Objective function cost

nonnegative scalar

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 only meaningful when the **nlp.status** output
is nonnegative.

#### Dependencies

To enable this port, select the **Optimal cost**
parameter.

`slack`

— Slack variable

0 | nonnegative scalar

Slack variable, ε, used in constraint softening, output as `0`

or
a positive scalar value.

ε = 0 — All soft constraints are satisfied over the entire prediction horizon.

ε > 0 — At least one soft constraint is violated. When more than one constraint is violated, ε represents the worst-case soft constraint violation (scaled by the ECR values for each constraint).

#### Dependencies

To enable this port, select the **Slack variable**
parameter.

`nlp.status`

— Optimization status

scalar

Optimization status, output as one of the following:

Positive Integer — Solver converged to an optimal solution

`0`

— Maximum number of iterations reached without converging to an optimal solutionNegative integer — Solver failed

#### Dependencies

To enable this port, select the **Optimization status**
parameter.

**Optimal Sequences**

`mv.seq`

— Optimal manipulated variable sequence

matrix

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*is the number of manipulated variables.

_{mv}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.

`x.seq`

— Optimal prediction model state sequence

matrix

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*is the number of states.

_{x}The first row of **x.seq** contains the current estimated state
values, either from the built-in state estimator or from the custom state estimation
block input **x[k|k]**. The next *p* rows of
**x.seq** contain the calculated optimal state values from time
*k*+1 to time *k*+*p*.

#### Dependencies

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

`y.seq`

— Optimal output variable sequence

matrix

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*is the number of output variables.

_{y}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

`Nonlinear MPC Controller`

— Controller object

`nlmpc`

object name

You must provide an `nlmpc`

object
that defines a nonlinear MPC controller. To do so, enter the name of an
`nlmpc`

object in the MATLAB workspace.

#### Programmatic Use

Block Parameter:
`nlmpcobj` |

Type: string, character vector |

Default:
`""` |

`Use prediction model sample time`

— Flag for using the prediction model sample time

on (default) | off

Select this parameter to run the controller using the same sample time as its
prediction model. To use a different controller sample time, clear this parameter, and
specify the sample time using the **Make block run at a different sample
time** parameter.

To limit the number of decision variables and improve computational efficiency, you can run the controller with a sample time that is different from the prediction horizon. For example, consider the case of a nonlinear MPC controller running at 10 Hz. If the plant and controller sample times match, predicting plant behavior for ten seconds requires a prediction horizon of length 100, which produces a large number of decision variables. To reduce the number of decision variables, you can use a plant sample time of 1 second and a prediction horizon of length 10.

#### Programmatic Use

Block Parameter:
`UseObjectTs` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"on"` |

`Make block run at a different sample time`

— Controller sample time

positive finite scalar

Specify this parameter to run the controller using a different sample time from its prediction model.

#### Dependencies

To enable this parameter, clear the **Use prediction model sample
time** parameter.

#### Programmatic Use

Block Parameter:
`TsControl` |

Type: string, character vector |

Default:
`""` |

`Use MEX to speed up simulation`

— Flag for simulating controller use MEX function

off (default) | on

Select this parameter to simulate the controller using a MEX function generated
using `buildMEX`

. Doing so reduces the simulation time of the
controller. To specify the name of the MEX function, use the **Specify MEX
function name** parameter.

#### Programmatic Use

Block Parameter:
`UseMEX` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Specify MEX function name`

— Controller MEX function name

string

Use this parameter to specify the name of the MEX function to use during simulation.
To create the MEX function, use the `buildMEX`

function.

#### Dependencies

To enable this parameter, select the **Use MEX to speed up
simulation** parameter.

#### Programmatic Use

Block Parameter:
`mexname` |

Type: string, character vector |

Default:
`""` |

### General Tab

`Measured disturbances`

— Add measured disturbance input port

off (default) | on

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_enabled` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Targets for manipulated variables`

— Add manipulated variable target input port

off (default) | on

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

#### Programmatic Use

Block Parameter:
`mvtarget_enabled` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Model parameters`

— Add model parameters input port

off (default) | on

If your controller uses optional parameters, you must select this parameter to add
the **params** output port to the block.

For more information on creating a parameter bus signal, see `createParameterBus`

.

#### Programmatic Use

Block Parameter:
`param_enabled` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Optimal cost`

— Add optimal cost output port

off (default) | on

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

#### Programmatic Use

Block Parameter:
`cost_enabled` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Optimal control sequence`

— Add optimal control sequence output port

off (default) | on

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

#### Programmatic Use

Block Parameter:
`mvseq_enabled` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Optimal state sequence`

— Add optimal state sequence output port

off (default) | on

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

#### Programmatic Use

Block Parameter:
`stateseq_enabled` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Optimal output sequence`

— Add optimal output sequence output port

off (default) | on

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

#### Programmatic Use

Block Parameter:
`ovseq_enabled` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Slack variable`

— Add slack variable output port

off (default) | on

Select this parameter to add the **slack** output port to the
block.

#### Programmatic Use

Block Parameter:
`slack_enabled` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Optimization status`

— Add optimization status output port

off (default) | on

Select this parameter to add the **nlp.status** output port to
the block.

#### Programmatic Use

Block Parameter:
`status_enabled` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

### Online Features Tab

`Lower OV limits`

— Add minimum OV constraint input port

off (default) | on

Select this parameter to add the **ov.min** input port to the
block.

#### Programmatic Use

Block Parameter:
`ov_min` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Upper OV limits`

— Add maximum OV constraint input port

off (default) | on

Select this parameter to add the **ov.max** input port to the
block.

#### Programmatic Use

Block Parameter:
`ov_max` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Lower MV limits`

— Add minimum MV constraint input port

off (default) | on

Select this parameter to add the **mv.min** input port to the
block.

#### Programmatic Use

Block Parameter:
`mv_min` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Upper MV limits`

— Add maximum MV constraint input port

off (default) | on

Select this parameter to add the **mv.max** input port to the
block.

#### Programmatic Use

Block Parameter:
`mv_max` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Lower MVRate limits`

— Add minimum MV rate constraint input port

off (default) | on

Select this parameter to add the **dmv.min** input port to the
block.

#### Programmatic Use

Block Parameter:
`mvrate_min` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Upper MVRate limits`

— Add maximum MV rate constraint input port

off (default) | on

Select this parameter to add the **dmv.max** input port to the
block.

#### Programmatic Use

Block Parameter:
`mvrate_max` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Lower state limits`

— Add minimum state constraint input port

off (default) | on

Select this parameter to add the **x.min** input port to the
block.

#### Programmatic Use

Block Parameter:
`state_min` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Upper state limits`

— Add maximum state constraint input port

off (default) | on

Select this parameter to add the **x.max** input port to the
block.

#### Programmatic Use

Block Parameter:
`state_max` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`OV weights`

— Add OV tuning weights input port

off (default) | on

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

#### Programmatic Use

Block Parameter:
`ov_weight` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`MV weights`

— Add MV tuning weights input port

off (default) | on

Select this parameter to add the **mv.wt** input port to the
block.

#### Programmatic Use

Block Parameter:
`mv_weight` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`MVRate weights`

— Add MV rate tuning weights input port

off (default) | on

Select this parameter to add the **dmv.wt** input port to the
block.

#### Programmatic Use

Block Parameter:
`mvrate_weight` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`ECR weight`

— Add ECR tuning weight input port

off (default) | on

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

#### Programmatic Use

Block Parameter:
`ecr_weight` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

`Initial guess`

— Add initial guess input ports

off (default) | on

Select this parameter to add the **mv.init**,
**x.init**, and **e.init** input ports to the
block.

**Note**

By default, the Nonlinar MPC Controller block uses the calculated optimal manipulated variable and state trajectories from one control interval as the initial guesses for the next control interval.

Enable the initial guess ports only if it is necessary for your application.

#### Programmatic Use

Block Parameter:
`nlp_initialize` |

Type: string, character vector |

Values:
`"off"` , `"on"` |

Default:
`"off"` |

## Model Examples

## Extended Capabilities

### C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

Usage notes and limitations:

The Nonlinear MPC Controller block supports generating code only for nonlinear MPC controllers that use the default

`fmincon`

solver with the SQP algorithm.When used for code generation, nonlinear MPC controllers do not support expressing prediction model functions, stage cost functions or constraint functions as anonymous functions.

If your controller uses optional parameters, you must also generate code for the Bus Creator block connected to the

**params**input port. To do so, place the Nonlinear MPC Controller and Bus Creator blocks within a subsystem, and generate code for that subsystem.

**Introduced in R2018b**

## MATLAB 命令

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