【摘要】 跟踪伺服系统设计是APT核心技术,决定了通信链路能否建立以及通信系统的性能。针对精跟踪伺服系统设计中光斑抖动时变不确定性和PZT驱动中存在的迟滞、蠕变和位移非线性等不足,采用RBF神经网络自学习方法,控制PZT驱动精瞄偏转镜偏转,最终达到精跟踪伺服系统优化控制。通过16km链路的空间光通信实验表明:抑制到达角起伏后,最大起伏幅度抑制了80%和起伏均方差减少4倍,光纤耦合效率提高了6倍,光功率相对波动幅度明显减小;RBF神经网络控制算法增强了伺服系统鲁棒性,并提高了伺服系统实时性。更多还原

【Abstract】 The design of fine tracking servo system is the kernel technology of APT(Acquisition,Pointing and Tracking),which determines whether the communication link can be set up and the performance of the communication system.To solve the control problems of uncertainty of beacon vibration,creep resistance and nonlinear long lagging of FSM(fine pointing steering mirror),a fine tracking servo system is designed using RBF neural network.With a PID-like neural network algorithm,the weights and parameters of the neural network could be adjusted to reduce beacon vibration using the functions of self-learning and adaptability in real time;and then the on line optimization control of the mirror is achieved using the PZT actuator.The results of 16km-link free-space optical communication experiment show that with tracking,the maximum fluctuation amplitude is suppressed by 80%,the mean square deviation of the fluctuation is reduced to one fourth and the fiber coupling efficiency is increased by 6 times.Moreover the fluctuation of the received light power is decreased obviously.The proposed RBF neural network control algorithm enhances the robustness of the servo system and improves its real time capability.更多还原