The prediction model of a nonlinear MPC controller consists of the following userdefined functions:
State function — Predicts how the plant states evolve over time
Output function — Calculates plant outputs in terms of state and input variables
You can specify either a continuoustime or a discretetime prediction model.
Before simulating your controller, it is best practice to validate your custom functions,
including the state function, output function, and their Jacobians using the validateFcns
command.
You can specify either a continuoustime or a discretetime state function. For a:
Continuoustime prediction model, the state function is the state derivative function.
$$dx/dt=f\left(x,u\right)$$
Discretetime prediction model, the state function is the state update function.
$$x\left(k+1\right)=f\left(x\left(k\right),u\left(k\right)\right)$$
Since a nonlinear MPC controller is a discretetime controller, if your state function is continuoustime, the controller automatically discretizes the model using the implicit trapezoidal rule. This method can handle moderately stiff models, and its prediction accuracy depends on the controller sample time; that is, a large sample time can potentially lead to inaccurate prediction.
If the default discretization method does not provide satisfactory prediction for your application, you can specify your own discretetime prediction model that uses a different method. To do so, you can integrate a continuoustime state function from the given initial condition, x_{k}, to the next state, x_{k+1}. When doing so numerically, avoid approaches that require iterations, such as some variablestepsize methods, because these methods introduce numerical noise that degrades solver performance. An explicit multistep Euler method with sufficiently small step size is often the best method to try first. For an example, see Swingup Control of a Pendulum Using Nonlinear Model Predictive Control.
You can specify your state function in one of the following ways.
Name of a function in the current working folder or on the MATLAB^{®} path, specified as a string or character vector
Model.StateFcn = "myStateFunction";
Handle to a function in the current working folder or on the MATLAB path
Model.StateFcn = @myStateFunction;
Anonymous function
Model.StateFcn = @(x,u,params) myStateFunction(x,u,params)
Your state function must have one of the following signatures.
If your controller does not use optional parameters:
function z = myStateFunction(x,u)
If your controller uses parameters. Here, params
is a
commaseparated list of parameters:
function z = myStateFunction(x,u,params)
This table describes the inputs and outputs of this function, where:
N_{x} is the number of states and is equal to
the Dimensions.NumberOfStates
property of the controller.
N_{u} is the number of inputs, including all
manipulated variables, measured disturbances, and unmeasured disturbances, and is equal
to the Dimensions.NumberOfInputs
property of the controller.
Argument  Input/Output  Description 

x  Input  Current states, specified as a column vector of length N_{x}. 
u  Input  Current inputs, specified as a column vector of length N_{u}. 
params  Input  Optional parameters, specified as a commaseparated list (for example
If your model uses optional parameters, you must specify
the number of parameters using the 
z  Output  State function output, returned as a column vector of length
N_{x}. For a continuoustime prediction
model, z contains the state derivatives,
dx/dt , and for discretetime prediction models,
z contains the next states, x(k+1) . 
As an example of a state function, consider the continuoustime model with the following state equations:
$$\begin{array}{l}{\dot{x}}_{1}={x}_{4}\\ {\dot{x}}_{2}={x}_{5}\\ {\dot{x}}_{3}={x}_{6}\\ {\dot{x}}_{4}=\left({u}_{1}{u}_{2}+{u}_{3}{u}_{4}\right)\mathrm{cos}\left({x}_{3}\right)\\ {\dot{x}}_{5}=\left({u}_{1}{u}_{2}+{u}_{3}{u}_{4}\right)\mathrm{sin}\left({x}_{3}\right)\\ {\dot{x}}_{6}=0.2\left({u}_{1}{u}_{2}{u}_{3}+{u}_{4}\right)\end{array}$$
You can specify the state function as follows:
z = zeros(6,1); z(1) = x(4); z(2) = x(5); z(3) = x(6); z(4) = (u(1)  u(2) + u(3)  u(4))*cos(x(3)); z(5) = (u(1)  u(2) + u(3)  u(4))*sin(x(3)); z(6) = 0.2*(u(1)  u(2)  u(3) + u(4));
To improve computational efficiency, it is best practice to specify an analytical
Jacobian for your state function. If you do not specify a Jacobian, the controller
computes the Jacobian using numerical perturbation. To specify a Jacobian for your state
function, set the Jacobian.StateFcn
property of the controller to one
of the following.
Name of a function in the current working folder or on the MATLAB path, specified as a string or character vector
Model.StateFcn = "myStateJacobian";
Handle to a function in the current working folder or on the MATLAB path
Model.StateFcn = @myStateJacobian;
Anonymous function
Model.StateFcn = @(x,u,params) myStateJacobian(x,u,params)
Your state Jacobian function must have one of the following signatures.
If your controller does not use optional parameters:
function [A,Bmv] = myStateJacobian(x,u)
If your controller uses parameters. Here params
is a
commaseparated list of parameters:
function [A,Bmv] = myStateJacobian(x,u,params)
The input arguments of the state Jacobian function are the same as the inputs of the state function. This table describes the outputs of the Jacobian function, where:
N_{x} is the number of states and is equal
to the Dimensions.NumberOfStates
property of the controller
N_{u} is the number of inputs, including
all manipulated variables, measured disturbances, and unmeasured disturbances, and is
equal to the Dimensions.NumberOfInputs
property of the
controller
Argument  Description 

A  Jacobian of the state function output, z , with respect to
x , returned as an
N_{x}byN_{x}
array, where $$\text{A}\left(i,j\right)=\partial \text{z}\left(i\right)/\partial \text{x}\left(j\right)$$. 
Bmv  Jacobian of the state function output with respect to the manipulated
variables, specified as an
N_{x}byN_{mv}
array, where $$\text{Bmv}\left(i,j\right)=\partial \text{z}\left(i\right)/\partial \text{u}\left(MV\left(j\right)\right)$$ and MV(j) is the
jth MV index in the Dimensions.MVIndex
controller property. Bmv contains the gradients with respect to
only the manipulated variables in u , since the measured and
unmeasured disturbances are not decision variables. 
Consider again, the state function with the following state equations:
$$\begin{array}{l}{\dot{x}}_{1}={x}_{4}\\ {\dot{x}}_{2}={x}_{5}\\ {\dot{x}}_{3}={x}_{6}\\ {\dot{x}}_{4}=\left({u}_{1}{u}_{2}+{u}_{3}{u}_{4}\right)\mathrm{cos}\left({x}_{3}\right)\\ {\dot{x}}_{5}=\left({u}_{1}{u}_{2}+{u}_{3}{u}_{4}\right)\mathrm{sin}\left({x}_{3}\right)\\ {\dot{x}}_{6}=0.2\left({u}_{1}{u}_{2}{u}_{3}+{u}_{4}\right)\end{array}$$
To find the Jacobians, compute the partial derivatives of the state equations with respect to the states and manipulated variables, assuming that all four inputs are manipulated variables.
A = zeros(6,6); A(1,4) = 1; A(2,5) = 1; A(3,6) = 1; A(4,3) = (u(1)  u(2) + u(3)  u(4))*sin(x(3)); A(5,3) = (u(1)  u(2) + u(3)  u(4))*cos(x(3)); B = zeros(6,4); B(4,:) = cos(x(3))*[1 1 1 1]; B(5,:) = sin(x(3))*[1 1 1 1]; B(6,:) = 0.2*[1 1 1 1];
The output function of your prediction model relates the states and inputs at the
current control interval to the outputs. If the number of states and outputs of the
prediction model are the same, you can omit OutputFcn
, which implies
that all states are measurable; that is, each output corresponds to one state.
Note
OutputFcn
cannot have direct feedthrough from any manipulated
variable to any output at any time; in other words, nonlinear MPC always assumes
Dmv = 0
.
You can specify your output function in one of the following ways.
Name of a function in the current working folder or on the MATLAB path, specified as a string or character vector
Model.OutputFcn = "myOutputFunction";
Handle to a function in the current working folder or on the MATLAB path
Model.OutputFcn = @myOutputFunction;
Anonymous function
Model.OutputFcn = @(x,u,params) myOutputFunction(x,u,params)
Your output function must have one of the following signatures.
If your controller does not use optional parameters:
function y = myOutputFunction(x,u)
If your controller uses parameters. Here, params
is a
commaseparated list of parameters:
function y = myOutputFunction(x,u,params)
This table describes the inputs and outputs of this function, where:
N_{x} is the number of states and is equal to
the Dimensions.NumberOfStates
property of the controller.
N_{u} is the number of inputs, including all
manipulated variables, measured disturbances, and unmeasured disturbances, and is equal
to the Dimensions.NumberOfInputs
of the controller.
N_{Y} is the number of outputs and is equal
to the Dimensions.NumberOfOutputs
property of the controller.
Argument  Input/Output  Description 

x  Input  Current states, specified as a column vector of length N_{x}. 
u  Input  Current inputs, specified as a column vector of length N_{u}. 
params  Input  Optional parameters, specified as a commaseparated list (for example
If your model uses optional parameters, you must specify
the number of parameters using

y  Output  Current outputs, returned as a column vector of length N_{y}. 
As an example of an output function, consider the following output equations. Recall, that your output function cannot have direct feedthrough from any manipulated variable to any output at any time.
$$\begin{array}{l}{y}_{1}={x}_{1}\\ {y}_{2}={x}_{2}+0.2{x}_{3}\\ {y}_{3}={x}_{3}\cdot {x}_{4}\end{array}$$
You can specify the output function as follows:
y = zeros(6,1); y(1) = x(1); y(2) = x(2)+0.2*x(3); y(3) = x(3)*x(4);
To improve computational efficiency, it is best practice to specify an analytical
Jacobian for your output function. If you do not specify a Jacobian, the controller
computes the Jacobian using numerical perturbation. To specify a Jacobian for your output
function, set the Jacobian.OutputFcn
property of the controller to one
of the following.
Name of a function in the current working folder or on the MATLAB path, specified as a string or character vector
Model.StateFcn = "myOutputJacobian";
Handle to a function in the current working folder or on the MATLAB path
Model.StateFcn = @myOutputJacobian;
Anonymous function
Model.StateFcn = @(x,u,params) myOutputJacobian(x,u,params)
Your output Jacobian function must have one of the following signatures.
If your controller does not use optional parameters:
function C = myOutputJacobian(x,u)
If your controller uses parameters. Here, params
is a
commaseparated list of parameters:
function C = myOutputJacobian(x,u,params)
The input arguments of the output Jacobian function are the same as the inputs of the output function. This table describes the output of the Jacobian function. Since the output function cannot have direct feedthrough from any manipulated variable to any output, the output Jacobian function returns only the gradients of the output function with respect to the model states.
Argument  Description 

C  Jacobian of the output function, returned as an N_{y}byN_{x} array, where $$\text{C}\left(i,j\right)=\partial \text{y}\left(i\right)/\partial \text{x}\left(j\right)$$. 
Consider again, the model with the following output equations:
$$\begin{array}{l}{y}_{1}={x}_{1}\\ {y}_{2}={x}_{2}+0.2{x}_{3}\\ {y}_{3}={x}_{3}\cdot {x}_{4}\end{array}$$
To find the Jacobians, compute the partial derivatives of the output equations with respect to the states. Since the output function cannot have direct feedthrough from any manipulated variable to any output at any time, you do not compute the Jacobian with respect to the manipulated variables.
C = zeros(3,4); C(1,1) = 1; C(2,2) = 1; C(2,3) = 0.2; C(3,3) = x(4); C(3,4) = x(3);
You can specify optional parameters for your nonlinear MPC prediction model, cost
function, and custom constraints. To do so, pass a commaseparated list of parameter
arguments to your custom functions (for example p1,p2,p3
). These
parameters must be numerical values. Also, you must specify the number of model parameters
using the Model.NumberOfParameters
property of the controller.
The same parameters are passed to the prediction model, custom cost function, custom
constraint functions, and their respective Jacobians. For example, even if the state
function uses only parameter p1
, the constraint functions use only
parameter p2
, and the cost function uses only parameter
p3
, you must still define three parameters. All of these parameters are
passed into all of these functions, and you must choose the correct parameter to use in each
function.
For an example that specifies the sample time of a discretetime state function as a parameter, see Swingup Control of a Pendulum Using Nonlinear Model Predictive Control.
You can augment your prediction model to include unmeasured disturbances. For example, if your plant does not have an integrator and you want to reject a certain unmeasured disturbance, augment your plan with a disturbance model. Doing so allows the external state estimator to detect the disturbance and thus give the nonlinear MPC controller enough information to reject it.
During simulation, the controller passes a zero to each unmeasured disturbance input channel, since the signals are unmeasured and assumed to be zeromean by default.
To augment your prediction model, you must designate one or more input signals as
unmeasured disturbances when creating a controller using nlmpc
. For
example, create a controller where the first two inputs are manipulated variables and the
third input is an unmeasured disturbance (UD).
nlobj = nlmpc(nx,ny,'MV',[1 2],'UD',3);
Specify the unmeasured disturbance model in the state and output functions of your prediction model. This unmeasured disturbance model can be any arbitrary model that accurately captures the effect of the disturbance on your plant. For example:
If you expect a steplike UD at a plant output, then specify the UD model as an integrator in your state function, and add the integrator state to the plant output in your output function.
If you expect a ramplike UD at a plant input, then specify the UD model as an integrator and add the integrator output to the input signal in your state function.
Any states that you add when specifying the unmeasured disturbance model are included in
the prediction model state vector. The values in these unmeasured disturbance model states
reflect the disturbance behavior during simulation. This state vector corresponds to the
x
input argument of your state function and the X
input argument to your custom cost and constraint functions.
For an example that augments the prediction model for random steplike output disturbances, see Nonlinear Model Predictive Control of an Exothermic Chemical Reactor.