Comparar métodos

	Controle por Retrocesso (Backstepping Control)	Controle Iterativo de Aprendizagem	Controle Preditivo por Modelo
Área	Teoria de controle	Teoria de controle	Teoria de controle
Família	Machine learning	Machine learning	Machine learning
Ano de origem	1995	1984	1978
Autor original	Miroslav Krstic	Suguru Arimoto	Jacques Richalet
Tipo	algorithm	algorithm	algorithm
Fonte seminal	Krstic, M., Kanellakopoulos, I., & Kokotovic, P. (1995). Nonlinear and Adaptive Control Design. John Wiley & Sons.	Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operation of robots by learning. Journal of Robotic Systems, 1(2), 123-140.	Richalet, J., Rault, A., Testud, J., & Papon, J. (1978). Model predictive heuristic control. Automatica, 14(5), 413-428.
Outros nomes	Integrator Backstepping, Recursive Lyapunov Design	ILC, Learning Control, Repetitive Control	MPC, Receding Horizon Control
Relacionados	3	4	5
Resumo	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.	Iterative Learning Control (ILC) is a control method for systems that perform the same task repeatedly (trajectory tracking over a fixed time interval). The key idea is to use error information from previous trials to update the input for the next trial, progressively improving tracking accuracy. Pioneered by Arimoto et al. in 1984, ILC is ideal for robotic manufacturing, semiconductor processing, and any application where the same motion must be repeated many times with high precision.	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.