Neural network based adaptive rise control of tank gun systems

With the development of the all-electric fourth-generation tank, higher requirements have been put forward for the accuracy and response speed of tank gun control and the traditional tank gun system modeling methods and control strategies failed to meet the requirements. For this reason, a neural network based adaptive robust integral of the error sign (ARISE) control for tank gun system is proposed considering two-axis dynamic coupling and nonlinear friction characteristics. Firstly, a two-axis coupling nonlinear dynamic model of the tank gun system is established and a continuous static friction model is used to characterize the nonlinear friction characteristics of the system. Then, an adaptive law is designed based on the desired position instruction to realize the online update of unknown system parameters. In addition, the neural network is used to compensate other unmodeled dynamic errors and the influence of neural network approximation error is suppressed by the nonlinear integral robust control term. The stability analysis results show that the proposed neural network based adaptive robust integral of the sign of the error control strategy can obtain excellent asymptotic tracking performance, and the simulation results verify its effectiveness.