Nonlinear hybrid adaptive inverse control using neural fuzzy system and its application to CSTR systems
A hybrid adaptive inverse control based on a neural fuzzy system is presented in this paper. It consists of two control loops, inverse control and PID control. PID control is a complement for inverse control and is mainly used to eliminate static error existing in direct inverse control when the inverse model is uncertain. For inverse control, the neural fuzzy system is utilized to construct the inverse controller (inverse model), which consists of two learning phases, improved nearest-neighborhood clustering algorithm and gradient descent algorithm. Finally, in order to test the effect of the proposed algorithm, it is applied to an exothermic CSTR. Simulation comparison with directed inverse control and PID control are also performed. The results show that the presented hybrid adaptive inverse control possesses better performance both in speediness and robustness and can be applied to a nonlinear system.