基于模糊神经网络水下机器人直接自适应控制

Direct Adaptive Control of Underwater Vehicles Based on Fuzzy Neural Networks

提出了基于广义动态模糊神经网络的水下机器人直接自适戍控制方法,该控制方法既不需要预先知道模糊神经结构,也不需要预先的训练阶段,完全通过在线自适应学习算法构建水下机器人的逆动力学模型.首先,本文提出了基于这种网络结构的水下机器人直接自适应控制器,然后,利用Lyapunov稳定理论,证明了基于该控制器的水下机器人控制系统闭环稳定性,最后,采用某水下机器人模型仿真验证了该控制方法的有效性。

A type of direct adaptive control method based on generalized dynamic fuzzy neural networks for underwater vehicles was proposed in this paper. The proposed control method, which needs neither prior fuzzy neural networks structure knowledge nor prior training phase, could be used to build the underwater vehicles inverse-dynamic model through online adaptive learning algorithm. The underwater vehicles direct adaptive controller based on this kind fuzzy neural networks is proposed in this paper, and then the stability of the resulting underwater vehicles closed-loop control system is proved using Lyaponov stability theory. The validity of the proposed control method has been verified through computer simulation experiments using an underwater vehicle model.