基于RBF的舰载武器稳定平台自适应控制

RBF Based Adaptive Control for Stabilized Platform of Shipborne Weapons

舰载武器稳定平台是一个非线性时变系统,采用传统的PID控制难以达到较好的控制性能指标要求。依据舰载武器稳定平台控制稳定回路数学模型及工作环境的特点,提出了基于RBF神经网络辨识的单神经元PID模型参考自适应控制策略,设计了舰载武器稳定平台控制系统。利用RBF神经网络作为辨识器,实现对被控对象Jacobian信息精确辨识,以基于Delta学习规则的单神经元自适应PID作为控制器。仿真结果表明,采用提出的控制策略,提高了系统跟踪精度、稳定精度和响应速度,增强了鲁棒性。

Stabilized platform of shipborne weapons is a time-varying,non-linear system,so it is difficult to meet the control request with the traditional PID control method.According to the mathematical model of control stabilization loop and the characteristics of operating conditions of stabilized platform,single neuron PID model reference adaptive control strategy based on RBF(radial basis function) neural network identification is introduced,and stabilized platform of shipborne weapons is designed.RBF neural network identification can identify the accurate Jacobian information, and single neuron adaptive PID controller based on Delta rule can obtain the stability.The simulation results show that the method not only enhances the fast tracking performance,but also has stronger robustness.