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Focusing on the unknown plant model, an adaptive wavelet neural network (WNN) identification robust control scheme of tank gun control system was proposed. According to input and output data, the unknown tank gun control system model parts were reconstructed using wavelet neural network (WNN). The parameters of online adaptive regulating law and output control laws were designed in the sense of Lyapunov, so the tank gun control system achieves dynamic decoupling. In order to reduce the effect of modeling errors and external disturbance, sliding mode variable structure control (SMVSC) was integrated into the adaptive control algorithm, and the robust compensator was added in order to achieve a robust tracking performance. Further the robust and steady performance of gun control system was analyzed. The results showed that wavelet neural network can be a good approximation nonlinear function, the system was insensitive to the parameters uncertainties and load disturbance and had shown a good track performance.

Research on Adaptive H Control of Tank Gun Control System Based on Wavelet Neural Network Identification