nlmpcmoveopt
Option set for nlmpcmove
function
Description
To specify options for the nlmpcmove
function,
use an nlmpcmoveopt
option set.
Using this option set, you can specify run-time values for a subset of controller
properties, such as tuning weights and constraints. If you do not specify a value for one of
the nlmpcmoveopt
properties, the corresponding value defined in the
nlmpc
controller
object is used instead.
Creation
Syntax
Description
creates a default set
of options for the options
= nlmpcmoveoptnlmpcmove
function. To modify the property values,
use dot notation.
Properties
OutputWeights
— Output variable tuning weights
[]
(default) | row vector | matrix
Output variable tuning weights that replace the
Weights.OutputVariables
property of the controller at run time,
specified as a row vector or matrix of nonnegative values.
To use the same weights across the prediction horizon, specify a row vector of length Ny, where Ny is the number of output variables.
To vary the tuning weights over the prediction horizon from time k+1 to time k+p, specify an array with Ny columns and up to p rows. Here, k is the current time and p is the prediction horizon. Each row contains the output variable tuning weights for one prediction horizon step. If you specify fewer than p rows, the weights in the final row are used for the remaining steps of the prediction horizon.
MVWeights
— Manipulated variable tuning weights
[]
(default) | row vector | matrix
Manipulated variable tuning weights that replace the
Weights.ManipulatedVariables
property of the controller at run
time, specified as a row vector or matrix of nonnegative values.
To use the same weights across the prediction horizon, specify a row vector of length Nmv, where Nmv is the number of manipulated variables.
To vary the tuning weights over the prediction horizon from time k to time k+p-1, specify an array with Nmv columns and up to p rows. Here, k is the current time and p is the prediction horizon. Each row contains the manipulated variable tuning weights for one prediction horizon step. If you specify fewer than p rows, the weights in the final row are used for the remaining steps of the prediction horizon.
MVRateWeights
— Manipulated variable rate tuning weights
[]
(default) | row vector | matrix
Manipulated variable rate tuning weights that replace the
Weights.ManipulatedVariablesRate
property of the controller at run
time, specified as a row vector or matrix of nonnegative values.
To use the same weights across the prediction horizon, specify a row vector of length Nmv, where Nmv is the number of manipulated variables.
To vary the tuning weights over the prediction horizon from time k to time k+p-1, specify an array with Nmv columns and up to p rows. Here, k is the current time and p is the prediction horizon. Each row contains the manipulated variable rate tuning weights for one prediction horizon step. If you specify fewer than p rows, the weights in the final row are used for the remaining steps of the prediction horizon.
ECR Weight
— Slack variable tuning weight
[]
(default) | positive scalar
Slack variable tuning weight that replaces the Weights.ECR
property of the controller at run time, specified as a positive scalar.
OutputMin
— Output variable lower bounds
[]
(default) | row vector | matrix
Output variable lower bounds, specified as a row vector of length
Ny or a matrix with
Ny columns, where
Ny is the number of output variables.
OutputMin(:,i)
replaces the
OutputVariables(i).Min
property of the controller at run
time.
To use the same bounds across the prediction horizon, specify a row vector.
To vary the bounds over the prediction horizon from time k+1 to time k+p, specify a matrix with 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 final bounds are used for the remaining steps of the prediction horizon.
OutputMax
— Output variable upper bounds
[]
(default) | row vector | matrix
Output variable upper bounds, specified as a row vector of length
Ny or a matrix with
Ny columns, where
Ny is the number of output variables.
OutputMax(:,i)
replaces the
OutputVariables(i).Max
property of the controller at run
time.
To use the same bounds across the prediction horizon, specify a row vector.
To vary the bounds over the prediction horizon from time k+1 to time k+p, specify a matrix with 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 final bounds are used for the remaining steps of the prediction horizon.
MVMin
— Manipulated variable lower bounds
[]
(default) | row vector | matrix
Manipulated variable lower bounds, specified as a row vector of length
Nmv or a matrix with
Nmv columns, where
Nmv is the number of manipulated
variables. MVMin(:,i)
replaces the
ManipulatedVariables(i).Min
property of the controller at run
time.
To use the same bounds across the prediction horizon, specify a row vector.
To vary the bounds over the prediction horizon from time k to time k+p-1, specify a matrix with 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 final bounds are used for the remaining steps of the prediction horizon.
MVMax
— Manipulated variable upper bounds
[]
(default) | row vector | matrix
Manipulated variable upper bounds, specified as a row vector of length
Nmv or a matrix with
Nmv columns, where
Nmv is the number of manipulated
variables. MVMax(:,i)
replaces the
ManipulatedVariables(i).Max
property of the controller at run
time.
To use the same bounds across the prediction horizon, specify a row vector.
To vary the bounds over the prediction horizon from time k to time k+p-1, specify a matrix with 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 final bounds are used for the remaining steps of the prediction horizon.
MVRateMin
— Manipulated variable rate lower bounds
[]
(default) | row vector | matrix
Manipulated variable rate lower bounds, specified as a row vector of length
Nmv or a matrix with
Nmv columns, where
Nmv is the number of manipulated
variables. MVRateMin(:,i)
replaces the
ManipulatedVariables(i).RateMin
property of the controller at run
time. MVRateMin
bounds must be nonpositive.
To use the same bounds across the prediction horizon, specify a row vector.
To vary the bounds over the prediction horizon from time k to time k+p-1, specify a matrix with 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 final bounds are used for the remaining steps of the prediction horizon.
MVRateMax
— Manipulated variable rate upper bounds
[]
(default) | row vector | matrix
Manipulated variable rate upper bounds, specified as a row vector of length
Nmv or a matrix with
Nmv columns, where
Nmv is the number of manipulated
variables. MVRateMax(:,i)
replaces the
ManipulatedVariables(i).RateMax
property of the controller at run
time. MVRateMax
bounds must be nonnegative.
To use the same bounds across the prediction horizon, specify a row vector.
To vary the bounds over the prediction horizon from time k to time k+p-1, specify a matrix with 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 final bounds are used for the remaining steps of the prediction horizon.
StateMin
— State lower bounds
[]
(default) | row vector | matrix
State lower bounds, specified as a row vector of length
Nx or a matrix with
Nx columns, where
Nx is the number of states.
StateMin(:,i)
replaces the States(i).Min
property of the controller at run time.
To use the same bounds across the prediction horizon, specify a row vector.
To vary the bounds over the prediction horizon from time k+1 to time k+p, specify a matrix with 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 final bounds are used for the remaining steps of the prediction horizon.
StateMax
— State upper bounds
[]
(default) | row vector | matrix
State upper bounds, specified as a row vector of length
Nx or a matrix with
Nx columns, where
Nx is the number of states.
StateMax(:,i)
replaces the States(i).Max
property of the controller at run time.
To use the same bounds across the prediction horizon, specify a row vector.
To vary the bounds over the prediction horizon from time k+1 to time k+p, specify a matrix with 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 final bounds are used for the remaining steps of the prediction horizon.
MVTarget
— Manipulated variable targets
[]
(default) | row vector | matrix
Manipulated variable targets, specified as a row vector of length Nmv or a matrix with Nmv columns, where Nmv is the number of manipulated variables.
To use the same manipulated variable targets across the prediction horizon, specify a row vector.
To vary the targets over the prediction horizon (previewing) from time k to time k+p-1, specify a matrix with 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.
Parameters
— Parameter values
{}
(default) | cell vector
Parameter values used by the prediction model, custom cost function, and custom
constraints, specified as a cell vector with length equal to the
Model.NumberOfParameters
property of the controller. If the
controller has no parameters, then Parameters
must be
{}
.
The controller, nlmpcobj
, passes these parameters to the:
Model functions in
nlmpcobj.Model
(StateFcn
andOutputFcn
)Cost function
nlmpcobj.Optimization.CustomCostFcn
Constraint functions in
nlmpcobj.Optimization
(CustomEqConFcn
andCustomIneqConFcn
)Jacobian functions in
nlmpcobj.Jacobian
Passivity functions and their Jacobians in
nlmpcobj.Passivity
The order of the parameters must match the order defined for these functions.
X0
— Initial guesses for the optimal state solutions
[]
(default) | vector | matrix
Initial guesses for the optimal state solutions, specified as a row vector of length Nx or a matrix with Nx columns, where Nx is the number of states.
To use the same initial guesses across the prediction horizon, specify a row vector.
To vary the initial guesses over the prediction horizon from time k+1 to time k+p, specify a matrix with 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 final guesses are used for the remaining steps of the prediction horizon.
If X0
is []
, the default initial guesses are
the current states of the prediction model (x
input argument to
nlmpcmove
).
In general, during closed-loop simulation, you do not specify
X0
yourself. Instead, when calling nlmpcmove
,
return the opt
output argument, which is an
nlmpcmoveopt
object. opt.X0
contains the
calculated optimal state trajectories as initial guesses. You can then pass
opt
in as the options
input argument to
nlmpcmove
for the next control interval. These steps are a best
practice, even if you do not specify any other run-time options.
MV0
— Initial guesses for the optimal manipulated variable solutions
[]
(default) | vector | matrix
Initial guesses for the optimal manipulated variable solutions, specified as a row vector of length Nmv or a matrix with Nmv columns, where Nmv is the number of manipulated variables.
To use the same initial guesses across the prediction horizon, specify a row vector.
To vary the initial guesses over the prediction horizon from time k to time k+p-1, specify a matrix with 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 final guesses are used for the remaining steps of the prediction horizon.
If MV0
is []
, the default initial guesses
are the control signals used in the plant at the previous control interval
(lastmv
input argument to nlmpcmove
).
In general, during closed-loop simulation, you do not specify
MV0
yourself. Instead, when calling nlmpcmove
,
return the opt
output argument, which is an
nlmpcmoveopt
object. opt.MV0
contains the
calculated optimal manipulated variable trajectories as initial guesses. You can then
pass opt
in as the options
input argument to
nlmpcmove
for the next control interval. These steps are a best
practice, even if you do not specify any other run-time options.
Slack0
— Initial guess for the slack variable at the solution
[]
(default) | nonnegative scalar
Initial guess for the slack variable at the solution, specified as a nonnegative
scalar. If Slack0
is []
, the default initial
guess is 0
.
In general, during closed-loop simulation, you do not specify
Slack0
yourself. Instead, when calling nlmpcmove
,
return the opt
output argument, which is an
nlmpcmoveopt
object. opt.Slack
contains the
calculated slack variable as an initial guess. You can then pass opt
in as the options
input argument to nlmpcmove
for the next control interval. These steps are a best practice, even if you do not
specify any other run-time options.
Object Functions
nlmpcmove | Compute optimal control action for nonlinear MPC controller |
Examples
Specify Run-Time Parameters for Nonlinear MPC
Create a default nlmpcmoveopt
option set.
options = nlmpcmoveopt;
Specify the run-time values for the controller prediction model parameters. For this example, assume that the controller has the following optional parameters, which are input arguments to all the prediction model functions and custom functions of the controller.
Sample time of the model, specified as a single numeric value. Specify a value of
0.25
.Gain factors, specified as a two-element row vector. Specify a value of
[0.7 0.35]
.
The order in which you specify the parameters must match the order specified in the custom function argument lists. Also, the dimensions of the parameters must match the dimensions expected by the custom functions.
options.Parameters = {0.25,[0.7 0.35]};
To use these parameters when computing optional control actions for a nonlinear MPC controller, pass options
to the nlmpcmove
function.
Version History
Introduced in R2018b
See Also
Functions
Objects
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