Modeling for Dynamic Optimization
One of the benefits derived from a modeling effort is the direct application of the model for optimization. When solution robustness or speed are concerns, linearizations of the model may be practical. Other compromises of the model include excessive dependence on empirical formulations.
Model Predictive Control Tutorial
A basic Model Predictive Control (MPC) tutorial demonstrates the capability of a solver to determine a dynamic move plan. In this example, a linear dynamic model is used with the Excel solver to determine a sequence of manipulated variable (MV) adjustments that drive the controlled variable (CV) along a desired reference trajectory.
MATLAB Toolbox for Model Predictive Control
Model Predictive Control (MPC) predicts and optimizes time-varying processes over a future time horizon. This control package accepts linear or nonlinear models. Using large-scale nonlinear programming solvers such as APOPT and IPOPT, it solves data reconciliation, moving horizon estimation, real-time optimization, dynamic simulation, and nonlinear MPC problems.
Three example files are contained in this directory that implement a controller for Linear Time Invariant (LTI) systems:
- apm1_lti - translate any LTI model into APM format
- apm2_step - perform step tests to ensure model accuracy
- apm3_control - MPC setpoint change to new target values
APMonitor enables the use of empirical, hybrid, and fundamental models directly in control applications. The DBS file parameter imode is used to control the simulation mode. This option is set to 6 for nonlinear control.
NLC.imode = 6
Nonlinear control adjusts variables that are declared as Manipulated Variables (MVs) to meet an objective. The MVs are the handles that the optimizer uses to minimize an objective function.
The objective is formulated from Controlled Variables (CVs). The CVs may be controlled to a range, a trajectory, maximized, or minimized. The CVs are an expression of the desired outcome for the controller action.