Improved adaptive control for wing rock via fuzzy neural network with randomly assigned fuzzy membership function parameters

Two stable adaptive fuzzy-neural control schemes within the indirect and direct frameworks are proposed to suppress the wing rock occurring at high angles of attack. In the two control strategies, a fuzzy neural network (FNN) with any bounded nonconstant piecewise continuous membership function is used to approximate the system nonlinear dynamics and external disturbances. Differently from the existing techniques, the parameters of the fuzzy membership functions are determined based on the recently developed fuzzy-neural algorithm named online sequential fuzzy extreme learning machine (OS-Fuzzy-ELM) where the fuzzy membership function parameters need not be adjusted and could randomly be generated according to any given continuous probability distribution without any prior knowledge. This simplifies the design of the controllers. Furthermore to ensure stable control performance, the tuning laws of the consequent parameters are derived using the projection algorithm and Lyapunov stability theorem. The merits of the proposed control schemes lie in the simplicity, robustness and stability, which manifests they can be applied for online learning and real-time control. In order to evaluate the performance of the proposed two control schemes, a comparison between a neural control, a fuzzy control and a fuzzy-neural control is carried out on various initial conditions. Results indicate the performance of the proposed controllers is superior using the randomly assigned fuzzy membership function parameters.