Model Predictive Control (MPC) Algorithm

A Model Predictive Control (MPC) Algorithm is a process control algorithm that rely on dynamic process models (most often linear empirical models obtained by system identification).

• Example(s):
  • …
• Counter-Example(s):
  • Kalman Filtering Algorithm,
  • Locally Weighted Learning Algorithm.
• See: Integer Quadratic Programming Task, Linear-Quadratic Regulator, Process Control, Industrial Process, Chemical Plant, Oil Refineries, Power System, PID Controller.

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References

2016

• (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/Model_predictive_control Retrieved:2016-5-31.
  • Model predictive control (MPC) is an advanced method of process control that 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. 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. MPC has the ability to anticipate future events and can take control actions accordingly. PID and LQR 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.

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