Model Predictive Control for Tracking

Model predictive control (MPC) is a successful technique which enables to deliver
the desired goals specified for the controlled process. Having the prediction ability,
when combined with traditional feedback operation, it enables to make adjustments
that are stable and closer to the optimal of set-points. MPC controller often employs
cost functions with varying economic conditions. One to frequent changes in the
economics of the plant, the optimal steady states continuously changes. Funct ion of
MPC is to compute a sequence of control moves so that the predicted system
response moves as close to the set point by reducing the error. The objective of this
project is to analyze the response and the effect of tuning parameters on the control
system performance of tracking MPC controllers. The results of this project show the
effect of tuning parameters on the control system as well as the tracking ability for
different scenarios