Research on active disturbance rejection control of barrier-breaking weapon based on BP neural network optimization
A modified Active Disturbance Rejection Controller(ADRC)is designed to address the issue of various nonlinear dis-turbances affecting the response speed and tracking accuracy of a certain obstacle breaking weapon position servo system during opera-tion.By utilizing the advantages of good adaptability and self-learning ability of BP neural network(BPNN),the relevant parameters of ADRC are optimized online,and the artificial fish swarm algorithm(AFSA)is used to optimize the weights of the neural network,in order to further improve the performance of the controller.Using MATLAB software to simulate and verify the controller,the simu-lation results show that this control method can improve the anti-interference ability and tracking accuracy of the servo system.
servo systembarrier breaking weaponBP neural networkartificial fish swarm algorithmADRC
万方数据
维普
