Observer-based hybrid fuzzy CMAC controller for a class of uncertain chaotic systems

This paper presents an observer-based hybrid fuzzy cerebellar model articulation controller (CMAC) neural network control with a supervisory controller for a class of uncertain chaotic systems. This proposed control system is integrating sliding mode control (SMC) theory and CMAC neural network into fuzzy controller design. The total states of the chaotic system are not assumed to be available for measurement. A state observer is used to estimate unmeasured states of the systems. The supervised control is appended to assure that the fuzzy CMAC controller achieve a stable closed-loop system through Lyapunov stability theory. Finally, simulation results show that the effect of the approximation error on the tracking error can be attenuated efficiently by the proposed method.