Lecture 33 - Model Predictive Control
Model Predictive Control (MPC) uses a mathematical representation of the process to predict and manipulate the future response of a system. Instead of a feedback strategy like PID control, MPC is actively making compensating moves to stay within constraints, drive to an economic optimum, and maximize or minimize certain quantities. Lecture 33 is an introduction to MPC and multivariable control.
MPC requires the numerical solution of dynamic equations. Below is a tutorial on solving differential and algebraic equations.
We'll also take some time to review material for closed-loop control analysis. We formerly had an exam at this point in the semester but have replaced it with a second lab project. It is helpful to revisit the last couple weeks and get the big picture of where we are headed.
This review includes PID equations in the Laplace domain, stability analysis of proportional-only controllers, model predictive control, and optimization topics. A full listing of the topics are detailed in the attached worksheet.
- Course reading for next class: None
- Assignment due by the start of Lecture #34: Work on Lab Projects
Relate each problem in the context of the overall course objectives.
Model Predictive Control in Practice
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