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Model predictive control
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{{Short description|Advanced method of process 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 [[industrial process|process]] industries in [[chemical plant]]s and [[oil refineries]] since the 1980s. In recent years it has also been used in [[power system]] balancing models<ref>Arnold, Michèle; Andersson, Göran; "Model Predictive Control of energy storage including uncertain forecasts" https://www.pscc-central.org/uploads/tx_ethpublications/fp292.pdf</ref> and in [[power electronics]].<ref>Geyer, Tobias; ''Model predictive control of high power converters and industrial drives'', Wiley, London, {{ISBN|978-1-119-01090-6}}, Nov. 2016.</ref> 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 a linear–quadratic regulator ([[Linear–quadratic regulator|LQR]]). Also MPC has the ability to anticipate future events and can take control actions accordingly. [[PID controller|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.<ref>{{cite journal |last=Vichik |first=Sergey |last2=Borrelli |first2=Francesco |title=Solving linear and quadratic programs with an analog circuit |journal=Computers & Chemical Engineering |year=2014 |volume=70 |pages=160–171 |doi=10.1016/j.compchemeng.2014.01.011 |url=https://zenodo.org/record/896768}}</ref> [[Generalized predictive control]] (GPC) and [[dynamic matrix control]] (DMC) are classical examples of MPC.<ref name=wang />
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