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Everything about Model predictive control

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. 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 (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. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Model predictive controlA 5-minute orientation
  2. Skim a recent paper: Perceptive Locomotion Through Nonlinear Model-Predictive ControlInfluential work since 2022
  3. Pick up a classic book: Time Series AnalysisThe most-reprinted book on the subject
  4. Read the landmark paper: Model predictive heuristic controlCited 1,709 times
  5. Go sideways: Parametric programmingA closely related topic
  6. Go sideways: Optimal controlA closely related topic

📖 Definitions

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🗂️ The subject at a glance

  1. Overview
  2. Nonlinear MPC
  3. Explicit MPC
  4. Robust MPC
  5. MPC software
  6. MPC vs. LQR

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics