Nonlinear Model Predictive Control

Nonlinear Model Predictive Control#

Nonlinear Model Predictive Control (NMPC) is control strategy in which control inputs are determined by the solution of an optimization problem every time the plant is sampled.

Optimization Problem#

An explanation of the optimization problem solved in this implementation of NMPC is forthcoming.

Available Methods#

Class for performing NMPC simulations of IDAES flowsheets

class idaes.apps.caprese.nmpc.NMPCSim(plant_model=None, plant_time_set=None, controller_model=None, controller_time_set=None, inputs_at_t0=None, measurements=None, sample_time=None, **kwargs)[source]#

This is a user-facing class to perform NMPC simulations with Pyomo models for both plant and controller. The user must provide the models to use for each, along with sets to treat as “time,” inputs in the plant model, and measurements in the controller model. Its functionality is primarily to ensure that these components (as defined by the names relative to the corresponding provided models) exist on both models.