Economic model predictive controllers optimize control actions to satisfy generic economic or performance cost functions. The name Economic MPC derives from applications in which the cost function to minimize is the operating cost of the system under control.
Traditional implicit MPC controllers minimize a quadratic performance criterion (cost function) using a linear prediction model.
A quadratic cost function is adequate for tracking specified output and manipulated variable references. However, some applications can require optimizing for performance criteria, such as fuel consumption or production rates. Such performance criteria can be a combination of linear or nonlinear functions of the system states, inputs, or outputs.
An economic MPC controller:
Can use a linear or nonlinear prediction model
Uses your generic performance cost function instead of (or in addition to) the built-in quadratic cost function
Computes optimal control moves by solving a nonlinear optimization problem using the SQP algorithm in
To implement an economic MPC controller, create a nonlinear MPC controller object, and specify:
State and output functions that define your prediction model. For more information, see Specify Prediction Model for Nonlinear MPC.
A generic performance-based cost function. For more information, see Specify Cost Function for Nonlinear MPC.
For more information on nonlinear MPC controller objects, see
You can simulate economic MPC controllers:
In Simulink® using the Nonlinear MPC Controller block
At the command line using
Designing an economic MPC controller using the MPC Designer app is not supported.
An economic MPC controller requires Optimization Toolbox™ software.