Fuzzy adaptive control based on recurrent cerebellar neural network

In order to solve the control problem for a class of uncertain nonlinear system, a fuzzy adaptive control algorithm for cerebellar neural network was proposed. The system was divided into the nominal model, parameter uncertainty section and hybrid interference item including the modeling error, disturbances and un-modeled dynamic. The fuzzy adaptive control was adopted to approach every uncertain parameter of the system in real time, and the robustness control was used to eliminate the hybrid interference. In addition, the recurrent cerebellar model articulation controller (CMAC) was designed as an observer to approximate the upper boundary of the hybrid interference in real time. The uniformly bounded stability of the system was proved based on Lyapunov's theory. The simulation results for the attitude control of micro flying robot indicate that the proposed control algorithm improves the dynamic performance and robustness of the system. And the research conclusions can provide the basis for the effective control of complex nonliear systems.