ScholarGate
Asistent

Porovnat metody

Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.

Adaptivní řízení×Zpětné vkládání (Backstepping Control)×Iterative Learning Control×
OborTeorie řízeníTeorie řízeníTeorie řízení
RodinaMachine learningMachine learningMachine learning
Rok vzniku198319951984
TvůrceKarl J. AstromMiroslav KrsticSuguru Arimoto
Typalgorithmalgorithmalgorithm
Původní zdrojAstrom, K. J., & Wittenmark, B. (1983). Computer-Controlled Systems: Theory and Design. Prentice Hall. link ↗Krstic, M., Kanellakopoulos, I., & Kokotovic, P. (1995). Nonlinear and Adaptive Control Design. John Wiley & Sons. link ↗Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operation of robots by learning. Journal of Robotic Systems, 1(2), 123-140. DOI ↗
Další názvySelf-Tuning Control, Parameter Estimation ControlIntegrator Backstepping, Recursive Lyapunov DesignILC, Learning Control, Repetitive Control
Příbuzné334
ShrnutíAdaptive 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.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.
ScholarGateDatová sada
  1. v1
  2. 2 Zdroje
  3. PUBLISHED
  1. v1
  2. 1 Zdroje
  3. PUBLISHED
  1. v1
  2. 3 Zdroje
  3. PUBLISHED

Přejít na hledání Stáhnout prezentaci

ScholarGatePorovnat metody: Adaptive Control · Backstepping Control · Iterative Learning Control. Získáno 2026-06-17 z https://scholargate.app/cs/compare