基于模糊控制的潜器沉浮控制方法

　　针对水下潜器实际工作的需求和水下沉浮运动特征，提出一种基于自适应模糊控制的水下潜器自主沉浮控制方法。该方法从优化隶属函数入手，采用多层前向神经网络的误差反向传播(EBP)算法对它的参数进行在线修正，并采用Delta-Bar-Delta学习规则对学习速率进行在线调整，使EBP算法具有较快的收敛速度，同时避免了局部极小值问题。仿真实验表明，对于水下潜器自主沉浮运动的不能精确建摸、干扰严重的非线性与时变情况，这种自适应模糊控制是一种较好的控制方式。

　　Abstract:

　　As for actual operation requirement and characteristics of ups and downs movement of underwater submersible vehicle， an independent ups and downs control method based on adaptive fuzzy control was advanced. In this method， on optimization of membership function， EBP algorithm of multiple layers forward neural network was used to correct parameters of membership function， and learning rule of Delta-Bar-Delta was used to adjust learning speed， which mended convergence speed of EBP algorithm， and the problem of sectional minimum was avoided at the same time. Simulation experiments show that independent ups and downs movement of underwater submersible vehicle could not be modeled precisely， and disturbance had characteristics of serious nonlinear and time change， which could be improved by this adaptive fuzzy control method.