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相机稳定平台是作为飞行器航拍的一种外挂式设备,可以采用先进的控制策略避免飞机姿态变化和机身抖动对航拍质量造成的影响。常规的PID控制需要人为手动调节参数,提出一种基于RBF神经网络PID参数自整定的控制方法。通过设定初始PID参数,利用RBF神经网络自学习能力进行在线参数整定。仿真结果表明,与传统PID相比,RBF神经网络PID具有较高的精度和较强的适应性,平台跟踪精度可以达到3以内。
Camera Stabilization Platform is a kind of plug-in equipment for aircraft aerial photography. It can adopt advanced control strategy to avoid the influence of aircraft attitude change and body shake on aerial quality. Conventional PID control requires manual adjustment of parameters manually, and a self-tuning control method based on RBF neural network PID parameters is proposed. By setting the initial PID parameters, using RBF neural network self-learning ability online parameter tuning. The simulation results show that compared with the traditional PID, RBF neural network PID has higher precision and stronger adaptability, and the tracking accuracy of the platform can reach within 3.