In order to solve the problem of poor anti-interference when the obstacle breaking weapon is launched,an active dis-turbance rejection control method based on RBF and clustering algorithm is proposed.According to the principle of the following sys-tem of the barrier breaking weapon,the corresponding mathematical model is established.At the same time,in order to solve the prob-lem that the internal parameters of auto-disturbance rejection controller are too many and difficult to adjust,neural network(RBF)is used to adjust the parameters of extended observer(ESO)and nonlinear control rate(NLSEF)online,and improved clustering algo-rithm is used to adjust the parameters of RBF neural network online to achieve better control effect.Simulink simulation results show that the proposed method can improve the robustness and response speed of the gun control system,and enhance the anti-jamming a-bility of the system.
关键词
自抗扰控制器/扩张观测器/非线性控制率/神经网络/聚类算法
Key words
ADRC/extended state observe/nonlinear control rate/neural network/clustering algorithm
维普
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