Economic model predictive controllers optimize control actions to minimize a generic cost function under arbitrary nonlinear constraints. Using economic MPC, you can optimize your control system to satisfy an arbitrary performance index, such as fuel consumption or the operating cost of the system.
When you implement economic MPC, the controller:
Still uses a linear prediction model.
Uses your generic cost function instead of the built-in quadratic cost function.
Applies your nonlinear constraints in addition to any linear constraints you define in the controller object.
Computes optimal control moves by solving a nonlinear optimization
problem using the SQP algorithm in
Using economic MPC requires Optimization Toolbox™software.
Economic MPC controllers support simulation in MATLAB® and Simulink®, but they do not support code generation.
For more information, see Economic MPC.
|Create MPC controller|
|Optimal control action|
|Compute optimal control with prediction model updating|
|Compute gain-scheduling MPC control action at a single time instant|
|Options set for mpcmove and mpcmoveAdaptive|
|Simulate closed-loop/open-loop response to arbitrary reference and disturbance signals for implicit or explicit MPC|
|MPC simulation options|
Economic model predictive controllers optimize control actions to satisfy a generic nonlinear cost function under arbitrary nonlinear constraints.
Economic MPC controllers support generic cost functions, such as a combination of linear or nonlinear functions of the system states and inputs.
You can specify nonlinear constraints for your MPC application using a custom constraint function.
Maximize production of an ethylene oxide plant for profit using a nonlinear cost function and nonlinear constraints.