Neural network compensation technique for standard PD-like fuzzy controlled nonlinear systems

In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. A neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for time-varying effects. We study two neural-fuzzy control schemes based on two well-known neural network control schemes such as the FEL scheme and the RCT scheme. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.