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Kawalan Adaptif×Kawalan Backstepping×Kawalan Pembelajaran Berulang×
BidangTeori KawalanTeori KawalanTeori Kawalan
KeluargaMachine learningMachine learningMachine learning
Tahun asal198319951984
PengasasKarl J. AstromMiroslav KrsticSuguru Arimoto
Jenisalgorithmalgorithmalgorithm
Sumber perintisAstrom, 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 ↗
AliasSelf-Tuning Control, Parameter Estimation ControlIntegrator Backstepping, Recursive Lyapunov DesignILC, Learning Control, Repetitive Control
Berkaitan334
RingkasanAdaptive 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.
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ScholarGateBandingkan kaedah: Adaptive Control · Backstepping Control · Iterative Learning Control. Dicapai 2026-06-17 daripada https://scholargate.app/ms/compare