Design of Adaptive TSK Fuzzy Self-Organizing Recurrent Cerebellar Model Articulation Controller for Chaotic Systems Control

The synchronization and control of chaos have been under extensive study by researchers in recent years. In this study, an adaptive Takagi–Sugeno–Kang (TSK) fuzzy self-organizing recurrent cerebellar model articulation controller (ATFSORC) is proposed, which composed a set TSK rules, (CMAC), CMAC (RCMAC), (SOCMAC), compensation controller. Specifically, SOCMAC, RCMAC, laws are adopted so that the association memory layers ATFSORC can be modulated accordance with layer decision-making mechanism order to reduce structure complexity improve performance ATFSORC. Moreover, rules introduced increase learning speed ATFSORC, improved compensating designed dispel errors between ideal TFSORC. proposed applied chaotic systems validate its feasibility. Several simulation schemes demonstrated show effectiveness method. Simulation results obtain favorable when operated at different parameters. achieve faster convergence tracking error than (FCMAC) CMAC.