Loading Icon

Model predictive control

Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. In recent years it has also been used in power system balancing models and in power electronics. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. The main advantage of MPC is the fact that it allows the current timeslot to be optimized, while keeping future timeslots in account. This is achieved by optimizing a finite time-horizon, but only implementing the current timeslot and then optimizing again, repeatedly, thus differing from Linear-Quadratic Regulator (LQR). Also MPC has the ability to anticipate future events and can take control actions accordingly. PID controllers do not have this predictive ability. MPC is nearly universally implemented as a digital control, although there is research into achieving faster response times with specially designed analog circuitry.

Metrics Summary

Total Publications
Lifetime
16,518
Prior Five Years
7,114
Total Citations
Lifetime
260,876
Prior Five Years
64,069
Total Scholars
Lifetime
18,855
Prior Five Years
16,713

Institutional Rankings

Global (Worldwide)
Show More
National Institutional Rankings

Publications and Citation History

Publications based on Disciplines

Scholars based on Disciplines

Publications based on Fields

Scholars based on Fields

Highly Ranked Scholars™

Lifetime
Prior Five Years

Highly Cited Publications

Lifetime