Vertaile menetelmiä

	Backstepping-ohjaus	Malliohjattu säätö
Tieteenala	Säätöteoria	Säätöteoria
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1995	1978
Kehittäjä	Miroslav Krstic	Jacques Richalet
Tyyppi	algorithm	algorithm
Alkuperäislähde	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.
Rinnakkaisnimet	Integrator Backstepping, Recursive Lyapunov Design	MPC, Receding Horizon Control
Liittyvät	3	5
Tiivistelmä	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.