Model Predictive Control

Model predictive control (MPC) is an advanced method of process control that has been in use in the process industries since the 1980s. Model Predictive Control (MPC) is a multivariable control algorithm. Model predictive controllers rely on dynamic models of the process.

Traditional feedback controllers operate by adjusting control action in response to a change in the output set-point of a system. Model predictive control (MPC) is a technique that focuses on constructing controllers that can adjust the control action before a change in the output set-point actually occurs. This predictive ability, when combined with traditional feedback operation, enables a controller to make adjustments that are smoother and closer to the optimal control action values.

 

Model Predictive Control in LabVIEW:

PowerPoint used in the Video

 

Model Predictive Control in LabVIEW - Here we use LabVIEW for Model Predictive Control (MPC) Applications.

 

Example Code

 

 

LabVIEW PID + Kalman Filter + MPC Applications

In this application we will Control a Level Tank System using different control strategies:

• PID
• Use Kalman Filter for Estimation of Unknown Process Variables/Measurements
• PID + Feedforward (with help of Kalman Filter)
• MPC (+ Kalman Filter)

We will not go in depth of the theory/mathematics (but the basics will be presented), but focus on the practical implementation in LabVIEW.

 

Videos:

LabVIEW PID + Kalman Filter + MPC - Part 1 - YouTube Video

LabVIEW PID + Kalman Filter + MPC - Part 2 - YouTube Video

LabVIEW PID + Kalman Filter + MPC - Part 3 - YouTube Video

LabVIEW PID + Kalman Filter + MPC - Part 4 - YouTube Video

LabVIEW PID + Kalman Filter + MPC - Part 5 - YouTube Video

 

Resources:

PowerPoint used in the Videos

LabVIEW Code