Comparer des méthodes

	Commande prédictive par modèle	Arbre à exploration rapide aléatoire
Domaine	Théorie du contrôle	Théorie du contrôle
Famille	Machine learning	Machine learning
Année d'origine	1978	1998
Auteur d'origine	Jacques Richalet	Steven M. LaValle
Type	algorithm	algorithm
Source fondatrice	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.	LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical Report TR 98-11, Iowa State University.
Alias	MPC, Receding Horizon Control	RRT, Incremental Sampling-based Algorithm
Apparentées	5	3
Résumé	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.	The Rapidly-Exploring Random Tree (RRT) is a motion planning algorithm that builds a tree of feasible paths by iteratively sampling random configurations in the workspace and connecting them to the nearest existing node in the tree. Introduced by LaValle in 1998, RRT is a breakthrough for high-dimensional motion planning, enabling robots to find collision-free paths in complex environments with obstacles, joint limits, and kinematic constraints.