sim

Simulate an MPC controller in closed loop with a linear plant

Syntax

sim(mpcobj,Ns,r)

sim(mpcobj,Ns,r,v)

sim(___,SimOptions)

[y,t,u,xp,xc,SimOptions] = sim(___)

sim(ddobj,Ns,r)

sim(___,Name=Value)

[y,u,t] = sim(___)

Description

Use the Model Predictive Control Toolbox™ sim function to simulate, in discrete time, the closed-loop or open-loop response of a plant and an MPC controller with constraints and weights that do not change at run time. The MPC controller can be implicit, explicit or data-driven, the controlled plant must be linear and time-invariant, and you must specify the reference and disturbance signals in advance. For non data-driven controller, by default, the plant used in the simulation is the one in mpcobj.Model.Plant, but you can use a different plant model to assess the controller robustness to model mismatch.

To run simulink models programmatically instead, see sim (Simulink).

Classical or Explicit MPC Object

sim(mpcobj,Ns,r) simulates the closed-loop response to the specified reference signal, r. The simulation runs in discrete time, with sample time mpcobj.Ts, for Ns-1 simulation steps, and simulation results are plotted. The plant model is the one specified in mpcobj.Model.Plant (which is discretized or resampled, if needed). The MPC controller mpcobj can be either a classical MPC controller (mpc) or explicit MPC controller (explicitMPC). If you omit Ns or r then default values are used.

sim(mpcobj,Ns,r,v) also specifies the measured disturbance signal v.

sim(___,SimOptions) specifies additional simulation options. This syntax allows you to alter the default simulation options, such as initial states, input/output noise, and unmeasured disturbances, plant mismatch, etc. It also allows you to simulate the plant in open loop. You can use SimOptions with any of the previous input combinations.

[y,t,u,xp,xc,SimOptions] = sim(___) suppresses plotting and instead returns:

• the sequence of plant outputs y,

• the time sequence t (equally spaced by mpcobj.Ts),

• the manipulated variables u generated by the MPC controller,

• the sequence xp of states of the model of the plant used for simulation,

• the sequence xmpc of states of the MPC controller (provided by the state observer),

• and the simulation options object, SimOptions.

Data-driven MPC Object

sim(ddobj,Ns,r) simulates the closed-loop response to the specified reference signal, r. The simulation runs in discrete time, with sample time ddobj.Ts, for Ns-1 simulation steps, and simulation results are plotted. The MPC controller ddobj must be a DataDrivenMPC object.

sim(___,Name=Value)simulates the closed-loop response using additional name-value arguments. Use this syntax to specify input references, past inputs, or past outputs.

[y,u,t] = sim(___) suppresses plotting and instead returns:

• the sequence of plant outputs y

• the sequence of plant inputs u generated by the data-driven MPC controller

• the time sequence t (equally spaced by ddobj.Ts)

Examples

Simulate MPC Control of MISO Plant

Simulate the MPC control of a MISO system. The system has one manipulated variable, one measured disturbance, one unmeasured disturbance, and one output.

Create the continuous-time plant model. This plant will be used as the prediction model for the MPC controller.

sys = ss(tf({1,1,1},{[1 .5 1],[1 1],[.7 .5 1]}));

Discretize the plant model using a sampling time of 0.2 units.

Ts = 0.2;
sysd = c2d(sys,Ts);

Specify the MPC signal type for the plant input signals.

sysd = setmpcsignals(sysd,MV=1,MD=2,UD=3);

Create an MPC controller for the sysd plant model. Use default values for the weights and horizons.

mpcobj = mpc(sysd);

-->"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.

Constrain the manipulated variable to the [0 1] range.

mpcobj.MV = struct(Min=0,Max=1);

Specify the simulation stop time.

Tstop = 30;

Define the reference signal and the measured disturbance signal.

num_sim_steps = round(Tstop/Ts);
r = ones(num_sim_steps,1);
v = [zeros(num_sim_steps/3,1); ones(2*num_sim_steps/3,1)];

The reference signal, r, is a unit step. The measured disturbance signal, v, is a unit step, with a 10 unit delay.

Simulate the controller.

sim(mpcobj,num_sim_steps,r,v)

-->The "Model.Disturbance" property is empty:
   Integrator added as input disturbance model for unmeasured input #3.
   Assuming no disturbance added to measured output #1.
-->"Model.Noise" is empty. Assuming white noise on each measured output.

Input Arguments

mpcobj — Model predictive controller

mpc object | explicitMPC object

Model predictive controller, specified as one of the following:

• mpc object — Implicit MPC controller

• explicitMPC object — Explicit MPC controller created using generateExplicitMPC.

ddobj — Data-driven model predictive controller

DataDrivenMPC object

Data-driven model predictive controller, specified as a DataDrivenMPC object. To create a data-driven MPC controller, use DataDrivenMPC.

Example: ddobj = DataDrivenMPC((0.5-rand(100,1))*2,lsim(tf(1,[1 1]),(0.5-rand(100,1))*2,0.1*(1:100)',0),0.1); creates the DataDrivenMPC object ddobj from the input signal (0.5-rand(100,1))*2, and the output signal obtained by supplying the input signal to the LTI plant tf(1,[1 1]), with a time vector 0.1*(1:100)', a sample time of 0.1 seconds, and an initial state of 0.

Ns — Number of simulation points, including zero

positive integer

Number of simulation points, including zero, specified as a positive integer. The simulation runs for Ns-1 steps, between 0 and Ts*(Ns-1).

When the first input argument is an mpc or explicitMPC object, if you omit Ns, the default value is the number of rows of whichever of the following arrays has the largest number of rows:

• The input argument r

• The input argument v

• The UnmeasuredDisturbance property of SimOptions, if specified

• The OutputNoise property of SimOptions, if specified

• The greatest value between the prediction horizon mpcobj.P and 10.

When the first argument is a DataDrivenMPC object, you must specify Ns.

Example: 20

r — Reference signal

mpcobj.Model.Nominal.Y (default) | double array

Reference signal, specified as an array. This array has ny columns, where ny is the number of total (measured and unmeasured) plant outputs. r can have anywhere from 1 to Ns rows. If the number of rows is less than Ns, the missing rows are set equal to the last row.

When the first input argument is an mpc or explicitMPC object, if r is empty or unspecified, it defaults to the nominal output vector mpcobj.Model.Nominal.Y. When the first argument is a DataDrivenMPC object, you must specify r as a matrix of size Ns by ny.

Example: ones(20,3)

v — Measured input disturbance signal

mpcobj.Model.Nominal.U(md) (default) | double array

Measured input disturbance signal, specified as an array. This array has nv columns, where nv is the number of measured input disturbances. v can have anywhere from 1 to Ns rows. If the number of rows is less than Ns, the missing rows are set equal to the last row.

If v is empty or unspecified, it defaults to the nominal value of the measured input disturbance, mpcobj.Model.Nominal.U(md), where md is the vector containing the indices of the measured disturbance signals, as defined by setmpcsignals.

Example: [zeros(50,1);ones(50,1)]

SimOptions — Simulation options

[] (default) | mpcsimopt object

Simulation options, used to specify additional simulation options as well as noise and disturbance signals that feed into the plant but are unknown to the controller. You can also use this object to simulate the plant in open loop, or to specify a plant model to be used in simulation that is different from the one in mpcobj.Model.Plant, which allows you to assess the robustness of the control loop response to model mismatch.

For more information, see mpcsimopt.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: sim(ddobj,Ns,ref,MVTarget=ones(steps,1)); specifies an unit step value as reference plant input.

MVTarget — Plant input reference values for data-driven MPC

Ns-by-N_u array

Plant input reference values for data-driven MPC, specified as an Ns-by-N_u array, where N_u is the number of inputs and Ns is the number of simulation points. Defaults to zero if not specified.

The rows of the specified argument represent the input target values for the intervals from the current time k to the time Ns–1.

Example: MVTarget=0.2*ones(steps,1)

InitialInputs — Past plant input data

ddobj.PastSteps-by-N_u array

Past plant input data, specified as an ddobj.PastSteps-by-N_u array, where N_u is the number of inputs. Defaults to zero if not specified.

The rows of the specified argument represent the past input values for the intervals from the time k–ddobj.PastSteps to the time k–1, where k is the current time.

Example: InitialInputs=[2 3 4]'

InitialOutputs — Past plant output data

ddobj.PastSteps-by-N_y array

Past plant output data, specified as an ddobj.PastSteps-by-N_y array, where N_y is the number of outputs. Defaults to zero if not specified.

The rows of the specified argument represent the past output values for the intervals from the time k–ddobj.PastSteps to the time k–1, where k is the current time.

Example: InitialOutputs=[0.2 0.1 0.3]'

Output Arguments

y — Sequence of plant outputs values

double array

Sequence of plant outputs values, returned as a Ns-by-Ny array, where Ns is the number of simulation steps and Ny is the number of plant outputs. The values in y include neither additive output disturbances nor additive measurement noise (if any).

t — Time sequence

double column vector

Time sequence, returned as a Ns-by-1 array, where Ns is the number of simulation steps. The values in t are equally spaced by mpcobj.Ts.

u — Sequence of manipulated variables

double array

Sequence of manipulated variables values generated by the MPC controller, returned as a Ns-by-Nu array, where Ns is the number of simulation steps and Nu is the number of manipulated variables.

xp — Sequence of plant model states values

struct array

Sequence of plant model states values, returned as an Ns-by-Nxp array, where Ns is the number of simulation steps and Nxp is the number of states in the plant model. The plant model is either mpcobj.Model or SimOptions.Model, if the latter is specified.

xc — Controller states

struct array

Sequence of MPC controller states, returned as an Ns-by-1 structure array. Each entry in the structure array has the same fields as an mpcstate object. The controller uses a built-in linear Kalman filter to estimate the state of the plant, augmented by the disturbance and noise models. The state of the controller is the state of its internal Kalman filter. For open-loop simulations, xc is empty.

SimOptions — Simulation options object

mpcsimopt object

Simulation options objects used for the simulation. This object can specify noise and disturbance signals that feed into the plant but are unknown to the controller. It can also specify if the simulated system is open loop or if the plant model used in the simulation is different from the one in mpcobj.Model.Plant.

For more information, see mpcsimopt.

Version History

Introduced before R2006a