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.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- 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 ↗
- 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 ↗
- 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 ↗
Related methods
Referenced by
Active Disturbance Rejection ControlAdaptive ControlDirect Torque ControlFeedback LinearizationField-Oriented ControlH-infinity ControlHamilton-Jacobi-Bellman EquationIterative Learning ControlLinear Quadratic RegulatorPontryagin Maximum PrincipleProbabilistic RoadmapRapidly-Exploring Random TreeZiegler-Nichols Tuning