The study of wireless networked control systems based RBF neural network identification

Radial basis function neural network (RBFNN) is powerful computational tools that have been used extensively in the areas of pattern recognition, systems modeling and identification. Aiming to wireless networked control systems (WNCS) with time-variant, random and uncertain network delay, controlled plant model might be time-variant or nonlinear, a new approach is proposed that modified Smith predictor combined with adaptive PID control with RBF neural network identification, this approach can identify the controlled plant on-line, and the weights of the adaptive PID controller can be adjusted timely. The simulation result shows that the proposed method has the adaptability, strong robustness and satisfactory control performance requirement.