buildMEX
Syntax
Description
Linear MPC
Nonlinear MPC
builds a MEX file that solves a nonlinear MPC control problem faster than
mexNlnFcn
= buildMEX(nlobj
,mexName
,coreData
,onlineData
)nlmpcmove
. The MEX file is created in the current working
folder.
generates a MEX function using the code generation configuration object
mexFcn
= buildMEX(nlobj
,mexName
,coreData
,onlineData
,mexConfig
)mexConfig
. Use this syntax to customize your MEX code
generation.
Examples
Simulate Linear MPC Controller Using MEX File
Create a plant model and design an MPC controller for the plant, with sample time 0.1
.
plant = drss(1,1,1);plant.D = 0; mpcobj = mpc(plant,0.1);
-->"PredictionHorizon" is empty. Assuming default 10. -->"ControlHorizon" is empty. Assuming default 2. -->"Weights.ManipulatedVariables" is empty. Assuming default 0.00000. -->"Weights.ManipulatedVariablesRate" is empty. Assuming default 0.10000. -->"Weights.OutputVariables" is empty. Assuming default 1.00000.
Simulate the plant using mpcmove
for 5
steps.
x = 0; xc = mpcstate(mpcobj);
-->No sample time in plant model. Assuming controller's sample time of 0.1. -->Assuming output disturbance added to measured output #1 is integrated white noise. -->"Model.Noise" is empty. Assuming white noise on each measured output.
for i=1:5 % Update plant output. y = plant.C*x; % Compute control actions. u = mpcmove(mpcobj,xc,y,1); % Update plant state. x = plant.A*x + plant.B*u; end
Display the final value of the plant output, input and next state.
[y u x]
ans = 1×3
0.9999 0.3057 -2.3063
Generate data structures and build mex file.
[coreData,stateData,onlineData] = getCodeGenerationData(mpcobj);
mexfun = buildMEX(mpcobj, 'myMPCMex', coreData, stateData, onlineData);
Generating MEX function "myMPCMex" from linear MPC to speed up simulation. Code generation successful. MEX function "myMPCMex" successfully generated.
Simulate the plant using the mex file myMPCmex
, which you just generated, for 5
steps.
x=0; for i = 1:5 % Update plant output. y = plant.C*x; % Update measured output in online data. onlineData.signals.ym = y; % Update reference signal in online data. onlineData.signals.ref = 1; % Compute control actions. [u,stateData] = myMPCMex(stateData,onlineData); % Update plant state. x = plant.A*x + plant.B*u; end
Display the final value of the plant output, input and next state.
[y u x]
ans = 1×3
0.9999 0.3057 -2.3063
Simulate Nonlinear MPC Controller Using MEX File
Create a nonlinear MPC controller with four states, two outputs, and one input.
nlobj = nlmpc(4,2,1);
Zero weights are applied to one or more OVs because there are fewer MVs than OVs.
Specify the sample time and horizons of the controller.
Ts = 0.1; nlobj.Ts = Ts; nlobj.PredictionHorizon = 10; nlobj.ControlHorizon = 5;
Specify the state function for the controller, which is in the file pendulumDT0.m
. This discrete-time model integrates the continuous-time model defined in pendulumCT0.m
using a multistep forward Euler method.
nlobj.Model.StateFcn = "pendulumDT0";
nlobj.Model.IsContinuousTime = false;
The prediction model uses an optional parameter Ts
to represent the sample time. Specify the number of parameters and create a parameter vector.
nlobj.Model.NumberOfParameters = 1; params = {Ts};
Specify the output function of the model, passing the sample time parameter as an input argument.
nlobj.Model.OutputFcn = "pendulumOutputFcn";
Define standard constraints for the controller.
nlobj.Weights.OutputVariables = [3 3]; nlobj.Weights.ManipulatedVariablesRate = 0.1; nlobj.OV(1).Min = -10; nlobj.OV(1).Max = 10; nlobj.MV.Min = -100; nlobj.MV.Max = 100;
Validate the prediction model functions.
x0 = [0.1;0.2;-pi/2;0.3]; u0 = 0.4; validateFcns(nlobj,x0,u0,[],params);
Model.StateFcn is OK. Model.OutputFcn is OK. Analysis of user-provided model, cost, and constraint functions complete.
Only two of the plant states are measurable. Therefore, create an extended Kalman filter for estimating the four plant states. Its state transition function is defined in pendulumStateFcn.m
and its measurement function is defined in pendulumMeasurementFcn.m
.
EKF = extendedKalmanFilter(@pendulumStateFcn,@pendulumMeasurementFcn);
Define initial conditions for the simulation, initialize the extended Kalman filter state, and specify a zero initial manipulated variable value.
x0 = [0;0;-pi;0]; y0 = [x0(1);x0(3)]; EKF.State = x0; mv0 = 0;
Create code generation data structures for the controller, specifying the initial conditions and parameters.
[coreData,onlineData] = getCodeGenerationData(nlobj,x0,mv0,params);
Specify the output reference value in the online data structure.
onlineData.ref = [0 0];
Build a MEX function for solving the nonlinear MPC control problem. The MEX function is created in the current working directory.
mexFcn = buildMEX(nlobj,"myController",coreData,onlineData);
Generating MEX function "myController" from nonlinear MPC to speed up simulation. Code generation successful. MEX function "myController" successfully generated.
Run the simulation for 10
seconds. During each control interval:
Correct the previous prediction using the current measurement.
Compute optimal control moves using the MEX function. This function returns the computed optimal sequences in
onlineData
. Passing the updated data structure to the MEX function in the next control interval provides initial guesses for the optimal sequences.Predict the model states.
Apply the first computed optimal control move to the plant, updating the plant states.
Generate sensor data with white noise.
Save the plant states.
mv = mv0; y = y0; x = x0; Duration = 10; xHistory = x0; for ct = 1:(Duration/Ts) % Correct previous prediction xk = correct(EKF,y); % Compute optimal control move [mv,onlineData] = myController(xk,mv,onlineData); % Predict prediction model states for the next iteration predict(EKF,[mv; Ts]); % Implement first optimal control move x = pendulumDT0(x,mv,Ts); % Generate sensor data y = x([1 3]) + randn(2,1)*0.01; % Save plant states xHistory = [xHistory x]; end
Plot the resulting state trajectories.
figure subplot(2,2,1) plot(0:Ts:Duration,xHistory(1,:)) xlabel('time') ylabel('z') title('cart position') subplot(2,2,2) plot(0:Ts:Duration,xHistory(2,:)) xlabel('time') ylabel('zdot') title('cart velocity') subplot(2,2,3) plot(0:Ts:Duration,xHistory(3,:)) xlabel('time') ylabel('theta') title('pendulum angle') subplot(2,2,4) plot(0:Ts:Duration,xHistory(4,:)) xlabel('time') ylabel('thetadot') title('pendulum velocity')
Input Arguments
mpcobj
— Model predictive controller
mpc
object | explicitMPC
object
Model predictive controller, specified as one of the following:
mpc
object — Implicit MPC controllerexplicitMPC
object — Explicit MPC controller created usinggenerateExplicitMPC
.
nlobj
— Nonlinear model predictive controller
nlmpc
object | nlmpcMultistage
object
Nonlinear model predictive controller, specified as an nlmpc
or nlmpcMultistage
object.
Your controller must use the default fmincon
solver with the
SQP algorithm. Also, your controller must not use anonymous functions for its prediction
model, custom cost function, or custom constraint functions.
mexName
— MEX function name
string | character vector
MEX function name, specified as a string or character vector.
coreData
— Controller configuration parameters
structure
Nonlinear MPC configuration parameters that are constant at run time, specified as a
structure generated using getCodeGenerationData
.
stateData
— Linear controller state data structure
structure
Linear controller state data structure at run time, specified as a structure
generated using getCodeGenerationData
.
onlineData
— Initial online controller data
structure
Initial online controller data, specified as a structure generated using getCodeGenerationData
. For more information on setting the fields of
onlineData
, see nlmpcmoveCodeGeneration
.
mexConfig
— Code generation configuration object
MexCodeConfig
object
Code generation configuration object, specified as a
MexCodeConfig
object.
To create the configuration object, use the following code.
mexConfig = coder.config('mex');
To customize your MEX code generation, modify the settings of this object. For
example, to detect run-time memory access violations during debugging, set
IntegrityChecks
to true
.
mexConfig.IntegrityChecks = true;
By default, to improve the performance of the generated code, checks such as
IntegrityChecks
and ResponsivenessChecks
are
disabled by buildMEX
.
buildMEX
overwrites the following configuration settings with
the values indicated.
Configuration Setting | Value |
---|---|
cfg.DynamicMemoryAllocation | 'AllVariableSizeArrays' |
cfg.ConstantInputs | 'Remove' |
Output Arguments
mexLinFcn
— Generated MEX function for linear MPC
function handle
Generated MEX function for linear MPC, returned as a function handle. This MEX function has the following signature.
[mv,newStateData,info] = mexLinFcn(stateData,onlineData)
The MEX function has the following input arguments, which are the same as the
corresponding input arguments of mpcmoveCodeGeneration
, except configData
, which is
embedded in mexLinFcn
.
Input Argument | Description |
---|---|
stateData | Current state data. Structure containing the state of the linear MPC
controller. For more information on setting the fields of
|
onlineData | Online controller data that you must update at run time, specified as a
structure. Generate the initial structure using getCodeGenerationData . For more information on setting the fields
of onlineData , see mpcmoveCodeGeneration . |
The MEX function has the following output arguments, which are the same as the
output arguments of mpcmoveCodeGeneration
.
Output Argument | Description |
---|---|
mv | Optimal manipulated variable control action, returned as a column vector of length Nmv, where Nmv is the number of manipulated variables. |
newStateData | Updated state data. Structure containing the updated state of the
linear MPC controller. For more information on setting the fields of
|
info | Solution details, returned as a structure. |
To simulate a controller using the generated MEX function, use the initial online
data structure onlineData
for the first control interval. For
subsequent control intervals, modify the online data in
newOnlineData
and pass the updated structure to the MEX function
as onlineData
.
mexNlnFcn
— Generated MEX function for nonlinear MPC
function handle
Generated MEX function for nonlinear MPC, returned as a function handle. This MEX function has the following signature.
[mv,newOnlineData,info] = mexNlnFcn(x,lastMV,onlineData)
The MEX function has the following input arguments, which are the same as the
corresponding input arguments of nlmpcmoveCodeGeneration
.
Input Argument | Description |
---|---|
x | Current prediction model states, specified as a vector of length Nx, where Nx is the number of prediction model states. |
lastMV | Control signals used in plant at previous control interval, specified as a vector of length Nmv, where Nmv is the number of manipulated variables. |
onlineData | Online controller data that you must update at run time, specified as a
structure. Generate the initial structure using getCodeGenerationData . For more information on setting the fields
of onlineData , see nlmpcmoveCodeGeneration . |
The MEX function has the following output arguments, which are the same as the
output arguments of nlmpcmoveCodeGeneration
.
Output Argument | Description |
---|---|
mv | Optimal manipulated variable control action, returned as a column vector of length Nmv, where Nmv is the number of manipulated variables. |
newOnlineData | Updated online controller data, returned as a structure. This
structure is the same as |
info | Solution details, returned as a structure. |
To simulate a controller using the generated MEX function, use the initial online
data structure onlineData
for the first control interval. For
subsequent control intervals, modify the online data in
newOnlineData
and pass the updated structure to the MEX function
as onlineData
.
Version History
Introduced in R2020a
See Also
Functions
mpcmove
|nlmpcmove
|mpcmoveCodeGeneration
|nlmpcmoveCodeGeneration
|getCodeGenerationData
|codegen
(MATLAB Coder)
Objects
MATLAB-Befehl
Sie haben auf einen Link geklickt, der diesem MATLAB-Befehl entspricht:
Führen Sie den Befehl durch Eingabe in das MATLAB-Befehlsfenster aus. Webbrowser unterstützen keine MATLAB-Befehle.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)