This paper proposes an improved wavelet neural network-internal model controller (WNN-IMC) for the rocket launcher position servo system. Due to complex nonlinearities and uncertainties of external disturbances in the rocket launcher position servo system, it is vitally challenging to establish its accurate model by the mechanical modeling technique. A wavelet neural network (WNN) identification method is proposed to determine the system mathematical model through test datum, which optimized by the hybrid algorithm of differential evolution (DE) and particle swarm optimization (PSO). Then, the proposed method is applied to identify the semi-physical simulation platform of the rocket launcher velocity servo system. The results demonstrate that the validity of the DEPSO-WNN method is better than that of the WNN and PSO-WNN methods. Finally, compared with the WNN-IMC controller and the ADRC controller, the effectiveness of the improved WNN-IMC controller is verified by the semi-physical simulation experiments.
Internal Model Control Using a Self-Recurrent Wavelet Neural Network Trained by an Artificial Immune Technique for Nonlinear Systems
