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linear plant specification -凯发k8网页登录

specify linear plant model, input and output signal types, scale factors

model predictive controllers use linear models to control both linear and nonlinear plants that run within a local operating range. plants with complex characteristics such as long time delays, higher-order dynamics, or strong interactions are particularly well-suited for model predictive control. to create a linear plant model, you can directly specify a linear model, linearize a simulink® model, or identify a linear model using measured data. when creating a plant model for use in model predictive control, it is important to specify the input and output signal types and scale factors. for more information, see and .

functions

set signal types in lti plant model
retrieve i/o signal names from mpc plant model
set i/o signal names in mpc plant model

topics

model and signals


  • plant inputs are independent variables that affect the plant, and plant outputs are dependent variables that you want to control or monitor.
  • mpc prediction models
    model predictive controllers use plant, disturbance, and noise models for prediction and state estimation.

  • when designing an mpc controller, it is good practice to define scale factors for each plant input and output, especially when variables have large differences in magnitude.

obtain lti models


  • mpc controllers support the same lti model formats as control system toolbox™ software.

  • most mpc applications involve plants with multiple inputs and outputs.
  • linearize simulink models
    obtain a linear approximation of a nonlinear plant at a specified operating point.

  • open mpc designer from simulink and define the mpc structure by linearizing the model.

  • estimate a linear system identification toolbox™ model using measured input/output data.

  • description of a continuously stirred-tank reactor (cstr) involving an exothermic reaction.
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