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| Modellprädiktive Regelung× | Feedback-Linearisierung× | |
|---|---|---|
| Fachgebiet | Regelungstechnik | Regelungstechnik |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1978 | 1983 |
| Urheber≠ | Jacques Richalet | Alberto Isidori |
| Typ | algorithm | algorithm |
| Wegweisende Quelle≠ | Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428. DOI ↗ | Isidori, A. (1995). Nonlinear Control Systems (3rd ed.). Springer-Verlag. DOI ↗ |
| Aliasnamen≠ | MPC, Receding Horizon Control | Exact Linearization, Nonlinear Feedback Control, Input-Output Linearization |
| Verwandt≠ | 5 | 4 |
| Zusammenfassung≠ | 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. | Feedback Linearization is a nonlinear control technique that uses a nonlinear state-feedback transformation to convert a nonlinear system into a linear one, enabling the use of standard linear control methods. Developed by Isidori, Sontag, and others in the 1980s, feedback linearization is conceptually elegant and powerful: if the system satisfies certain structural conditions (relative degree, decoupling matrix rank), the nonlinearities can be exactly cancelled through feedback, reducing the problem to linear design. |
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