{"id":1237,"date":"2019-07-30T11:59:21","date_gmt":"2019-07-30T11:59:21","guid":{"rendered":"http:\/\/krakoras.net\/blog\/?p=1237"},"modified":"2019-07-30T11:59:21","modified_gmt":"2019-07-30T11:59:21","slug":"model-predictive-control","status":"publish","type":"post","link":"http:\/\/krakoras.net\/blog\/?p=1237","title":{"rendered":"Model Predictive Control"},"content":{"rendered":"

Model predictive control<\/b>\u00a0(MPC<\/b>) is an advanced method of\u00a0process control<\/a>\u00a0that is used to control a process while satisfying a set of constraints. It has been in use in the\u00a0process<\/a>\u00a0industries in\u00a0chemical plants<\/a>\u00a0and\u00a0oil refineries<\/a>\u00a0since the 1980s. In recent years it has also been used in\u00a0power system<\/a>\u00a0balancing models[1]<\/a><\/sup>\u00a0and in\u00a0power electronics<\/a>[2]<\/a><\/sup>. Model predictive controllers rely on dynamic models of the process, most often linear\u00a0empirical<\/a>\u00a0models obtained by\u00a0system identification<\/a>. 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<\/a>). Also MPC has the ability to anticipate future events and can take control actions accordingly.\u00a0PID<\/a>\u00a0controllers 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.[3]<\/a><\/sup><\/p>\n

www.vutbr.cz\/www_base\/zav_prace_soubor_verejne.php?file_id=67592<\/a><\/p>\n

apmonitor.com\/do\/index.php\/Main\/AdvancedTemperatureControl<\/a><\/p>\n