Documentation Help Center. A model predictive controller uses linear plant, disturbance, and noise models to estimate the controller state and predict future plant outputs. Using the predicted plant outputs, the controller solves a quadratic programming optimization problem to determine control moves.

For more information on the structure of model predictive controllers, see MPC Modeling. The controller, mpcobjinherits its control interval from plant. Tsand its time unit from plant. All other controller properties are default values. After you create the MPC controller, you can set its properties using dot notation.

If plant. If plant is a:. Continuous-time model, then the controller discretizes the model for prediction using sample time ts. A discrete-time model with a specified sample time, the controller resamples the plant for prediction using sample time ts. A discrete-time model with an unspecified sample time plant. If any of these values are omitted or empty, the default values apply.

P sets the PredictionHorizon property. M sets the ControlHorizon property. MV sets the ManipulatedVariables property. OV sets the OutputVariables property. DV sets the DisturbanceVariables property.

When you do not specify a sample time, the plant model, model. Plantmust be a discrete-time model. This syntax sets the Model property of the controller. If model. Plant is a discrete-time LTI model with an unspecified sample time model. The specified plant corresponds to the Model. Plant property of the controller. If you do not specify a sample time when creating your controller, plant must be a discrete-time model.

Direct feedthrough from manipulated variables to any output in plant is not supported.Documentation Help Center. A nonlinear model predictive controller computes optimal control moves across the prediction horizon using a nonlinear prediction model, a nonlinear cost function, and nonlinear constraints. Use this syntax if your model has no measured or unmeasured disturbance inputs.

Specify the input indices for the manipulated variables, mvIndexand measured disturbances, mdIndex. Specify the input indices for the manipulated variables and unmeasured disturbances, udIndex. Specify the input indices for the manipulated variables, measured disturbances, and unmeasured disturbances. Number of prediction model states, specified as a positive integer. You cannot change the number of states after creating the controller object. Number of prediction model outputs, specified as a positive integer.

You cannot change the number of outputs after creating the controller object. Number of prediction model inputs, which are all manipulated variables, specified as a positive integer.

You cannot change the number of manipulated variables after creating the controller object. Manipulated variable indices, specified as a vector of positive integers. You cannot change these indices after creating the controller object.

This value is stored in the Dimensions. MVIndex controller property. The combined set of indices from mvIndexmdIndexand udIndex must contain all integers from 1 through N uwhere N u is the number of prediction model inputs. Measured disturbance indices, specified as a vector of positive integers. MDIndex controller property.

matlab mpc disturbance model

Unmeasured disturbance indices, specified as a vector of positive integers. UDIndex controller property. Prediction model sample time, specified as a positive finite scalar. The controller uses a discrete-time model with sample time Ts for prediction.

Non stick cookware repair spray

If you specify a continuous-time prediction model Model. IsContinuousTime is truethen the controller discretizes the model using the built-in implicit trapezoidal rule with a sample time of Ts. Prediction horizon steps, specified as a positive integer. The product of PredictionHorizon and Ts is the prediction time; that is, how far the controller looks into the future.

Positive integer, mbetween 1 and pinclusive, where p is equal to PredictionHorizon. Here, k is the current control interval. Vector of positive integers [ m 1m 2…], specifying the lengths of blocking intervals. By default the controller computes M blocks of free moves, where M is the number of blocking intervals.

Using block moves can improve the robustness of your controller.Documentation Help Center. A model predictive controller uses linear plant, disturbance, and noise models to estimate the controller state and predict future plant outputs. Using the predicted plant outputs, the controller solves a quadratic programming optimization problem to determine control moves.

For more information on the structure of model predictive controllers, see MPC Modeling. The controller, mpcobjinherits its control interval from plant.

Tsand its time unit from plant. All other controller properties are default values. After you create the MPC controller, you can set its properties using dot notation. If plant.

If plant is a:. Continuous-time model, then the controller discretizes the model for prediction using sample time ts. A discrete-time model with a specified sample time, the controller resamples the plant for prediction using sample time ts.

Online filmi

A discrete-time model with an unspecified sample time plant. If any of these values are omitted or empty, the default values apply. P sets the PredictionHorizon property. M sets the ControlHorizon property. MV sets the ManipulatedVariables property. OV sets the OutputVariables property. DV sets the DisturbanceVariables property.

When you do not specify a sample time, the plant model, model. Plantmust be a discrete-time model. This syntax sets the Model property of the controller. If model. Plant is a discrete-time LTI model with an unspecified sample time model. The specified plant corresponds to the Model. Plant property of the controller. If you do not specify a sample time when creating your controller, plant must be a discrete-time model.

Direct feedthrough from manipulated variables to any output in plant is not supported. Prediction model, specified as a structure with the same format as the Model property of the controller.

If you do not specify a sample time when creating your controller, model. Plant must be a discrete-time model. Controller sample time, specified as a positive finite scalar.

matlab mpc disturbance model

The controller uses a discrete-time model with sample time Ts for prediction.Documentation Help Center. When simulating an implicit or explicit MPC controller using the sim function, you can specify additional simulation options using an mpcsimopt object. To specify nondefault values for the propertiesuse dot notation. Simulation plant model initial state, specified as a vector with length equal to the number states in the plant model used for the simulation.

Fall creek sutler

To use the default nominal state of the simulation plant model, set PlantInitialState to []. If you do not specify the Model option, then the plant model used for the simulation is the internal plant model from the controller.

In this case, the default initial controller state is equal to mpcobj. If you specify the Model option, then the plant model used for simulation is Model. In this case, the default initial controller state is equal to Model. MPC controller initial condition, specified as an mpcstate object. Unmeasured disturbance signal for simulating disturbances occurring at the unmeasured disturbance inputs of the simulation plant model, specified as an array with N ud columns and up to N t rows, where N ud is the number of unmeasured disturbances, and N t is the number of simulation steps.

If you specify fewer than N t rows, then the values in the final row of the array are extended to the end of the simulation. Manipulated variable noise signal for simulating load disturbances occurring at the manipulated variable inputs to the simulation plant model, specified as an array with N mv columns and up to N t rows, where N mv is the number of manipulated variables, and N t is the number of simulation steps.

Measured output noise signal for simulating disturbances occurring at the measured output of the simulation plant model, specified as an array with N y columns and up to N t rows, where N y is the number of measured outputs, and N t is the number of simulation steps.

Flag indicating whether to use reference previewing during simulation, specified as one of the following:. Flag indicating whether to use measured disturbance previewing during simulation, specified as one of the following:. Flag indicating whether to enable constraints during simulation, specified as one of the following:.

In this case, there is no plant-model mismatch. The specified plant must have the same input and output group configuration as mpcobj. To set this configuration, use setmpcsignals. Structure with fields Plant and Nominal — Simulate the controller using the specified plant Plant and nominal conditions Nominal.

Model sets the actual plant that the controller is simulated against not the internal prediction model of the controller. Use this option to specify a plant that differs from the controller internal plant model model mismatch. If you do not specify nominal conditions, Model.Documentation Help Center. You can modify input and output disturbance models, and the measurement noise model using the MPC Designer app and at the command line.

You can then adjust controller tuning weights to improve disturbance rejection. MPC attempts to predict how known and unknown events affect the plant output variables OVs.

matlab mpc disturbance model

Known events are changes in the measured plant input variables MV and MD inputs. The plant model of the controller predicts the impact of these events, and such predictions can be quite accurate. For more information, see MPC Modeling. The impacts of unknown events appear as errors in the predictions of known events.

These errors are, by definition, impossible to predict accurately. However, an ability to anticipate trends can improve disturbance rejection. For example, suppose that the control system has been operating at a near-steady condition with all measured OVs near their predicted values.

There are no known events, but one or more of these OVs suddenly deviates from its prediction. The controller disturbance and measurement noise models allow you to provide guidance on how to handle such errors.

Suppose that your plant model includes no unmeasured disturbance inputs. The MPC controller then models unknown events using an output disturbance model. As shown in MPC Modelingthe output disturbance model is independent of the plant, and its output adds directly to that of the plant model. In the Output Disturbance Model dialog box, in the Update the model drop-down list, select specifying a custom model channel by channel.

In the Specifications section, in the Disturbance column, select one of the following disturbance models for each output:. White Noise — Prediction errors are due to random zero-mean white noise. This option implies that the impact of the disturbance is short-lived, and therefore requires a modest, short-term controller response. Random Step-like — Prediction errors are due to a random step-like disturbance, which lasts indefinitely, maintaining a roughly constant magnitude. Such a disturbance requires a more aggressive, sustained controller response.

Random Ramp-like — Prediction errors are due to a random ramp-like disturbance, which lasts indefinitely and tends to grow with time. Such a disturbance requires an even more aggressive controller response. You can also specify the white noise input Magnitude for each disturbance model, overriding the assumption of unit variance. As you increase the noise magnitude, the controller responds more aggressively to a given prediction error.

The specified noise magnitude corresponds to the static gain in the SISO model for each type of noise. You can also view or modify the output disturbance model from the command line using getoutdist and setoutdist respectively. MPC also attempts to distinguish disturbances, which require a controller response, from measurement noise, which the controller should ignore. Using MPC Designeryou can specify the expected measurement noise magnitude and character.

In the Model Noise Model dialog box, in the Update the model drop-down list, select specifying a custom model channel by channel. In the Specifications section, in the Disturbance column, select a noise model for each measured output channel. The noise options are the same as the output disturbance model options.Documentation Help Center.

Duniya ki sabse ajeeb cheez

Use this syntax if you previously set a custom output disturbance model and you want to change back to the default model. For more information on the default output disturbance model, see MPC Modeling. Define a plant model with no direct feedthrough, and create an MPC controller for that plant. Construct the output disturbance model using these transfer functions.

Use a separate noise input for each output disturbance. The controller converts the continuous-time transfer function model, outdistinto a discrete-time state-space model. Remove the integrator from the second output channel. Construct the new output disturbance model by removing the second input channel and setting the effect on the second output by the other two inputs to zero. When removing an integrator from the output disturbance model in this way, use sminreal to make the custom model structurally minimal.

The integrator has been removed from the second channel. The disturbance models for channels 1 and 3 remain at their default values as discrete-time integrators.

matlab mpc disturbance model

Define a plant model with no direct feedthrough and create an MPC controller for that plant. A static gain of 0 for all output channels indicates that the output disturbances were removed.

Model predictive controller, specified as an MPC controller object.

L3.4 - Introduction to Model Predictive Control (MPC) - reference tracking

To create an MPC controller, use mpc. Custom output disturbance model, specified as a state-space sstransfer function tfor zero-pole-gain zpk model. The MPC controller converts the model to a discrete-time, delay-free, state-space model. Omitting model or specifying model as [] is equivalent to using setoutdist MPCobj,'integrators'.

Unit-variance white noise input signals. For custom output disturbance models, the number of inputs is your choice. Each disturbance model output is added to the corresponding plant output. This model, along with the input disturbance model if anygoverns how well the controller compensates for unmeasured disturbances and modeling errors.

This check is performed later in the MPC design process when the internal state estimator is constructed using commands such as sim or mpcmove. If the controller states are not fully observable, these commands will generate an error. To view the current output disturbance model, use the getoutdist command. A modified version of this example exists on your system.

Do you want to open this version instead? 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:.Documentation Help Center.

For more information on the default model, see MPC Modeling. Set the first input signal as a manipulated variable and the second input as an unmeasured disturbance. Set the first input signal as a manipulated variable and the other two inputs as unmeasured disturbances. An integrator has been added only to the first unmeasured input disturbance.

The other input disturbance uses a static unity gain to preserve state observability. Model predictive controller, specified as an MPC controller object.

Select a Web Site

To create an MPC controller, use mpc. Input disturbance model used by the model predictive controller, MPCobjreturned as a discrete-time, delay-free, state-space model. Unit-variance white noise input signals. By default, the number of inputs depends upon the number of unmeasured input disturbances and the need to maintain controller state observability.

For custom input disturbance models, the number of inputs is your choice. Each disturbance model output is sent to the corresponding plant unmeasured disturbance input. If MPCobj does not have any unmeasured disturbance, indist is returned as an empty state-space model. This model, in combination with the output disturbance model if anygoverns how well the controller compensates for unmeasured disturbances and modeling errors.

Input channels with integrated white noise added by default, returned as a vector of input indices. If you set indist to a custom input disturbance model using setindistchannels is empty. To specify a custom input disturbance model, use the setindist command. 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:. Select the China site in Chinese or English for best site performance.

Other MathWorks country sites are not optimized for visits from your location. Toggle Main Navigation. Off-Canvas Navigation Menu Toggle.


thoughts on “Matlab mpc disturbance model

Leave a Reply

Your email address will not be published. Required fields are marked *