Machine learningOptimal Control

Model Predictive Control

Model Predictive Control (MPC) is an advanced control strategy that uses an explicit process model to predict future system behavior over a finite horizon and solves an optimization problem at each control step. First formalized by Richalet et al. in 1978, MPC has become the dominant approach in process control industries, from chemical plants to autonomous vehicles, because it naturally handles constraints and can optimize multiple objectives simultaneously.

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Sources

  1. Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428. DOI: 10.1016/0005-1098(78)90001-8
  2. Garcia, C. E., Prett, D. M., & Morari, M. (1989). Model predictive control: Theory and practice. Automatica, 25(3), 335-348. DOI: 10.1016/0005-1098(89)90002-2
  3. Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. (2000). Constrained model predictive control: Stability and optimality. Automatica, 36(6), 789-814. DOI: 10.1016/S0005-1098(99)00214-9

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Referenced by

ScholarGateModel Predictive Control (Model Predictive Control). Retrieved 2026-06-04 from https://scholargate.app/en/control-theory/model-predictive-control