Comparer des méthodes

	Commande Adaptative	Commande par "backstepping"	Commande prédictive par modèle
Domaine	Théorie du contrôle	Théorie du contrôle	Théorie du contrôle
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1983	1995	1978
Auteur d'origine	Karl J. Astrom	Miroslav Krstic	Jacques Richalet
Type	algorithm	algorithm	algorithm
Source fondatrice	Astrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall.	Krstic, M., Kanellakopoulos, I., & Kokotovic, P. (1995). Nonlinear and Adaptive Control Design. John Wiley & Sons.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Alias	Self-Tuning Control, Parameter Estimation Control	Integrator Backstepping, Recursive Lyapunov Design	MPC, Receding Horizon Control
Apparentées	3	3	5
Résumé	Adaptive Control is a control strategy that adjusts controller parameters in real-time based on online system identification to maintain performance despite changing plant dynamics or uncertain parameters. Pioneered by Astrom and Wittenmark, adaptive control enables robust operation in time-varying environments, from aircraft with fuel depletion to industrial systems with aging components.	Backstepping is a systematic nonlinear control design method that decomposes a complex nonlinear system into simpler subsystems and designs a controller recursively, layer by layer, ensuring stability at each step. Developed by Krstic, Kanellakopoulos, and Kokotovic, backstepping enables control of nonlinear systems without requiring exact model knowledge or full state linearization, combining flexibility with guaranteed stability.	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.