Adaptive output feedback control based on DRFNN for AUV

Adaptive output feedback control based on DRFNN for AUV
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基于DRFNN的AUV自适应输出反馈控制

DOI:
10.1016/j.oceaneng.2009.03.011
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发表时间:
2009-07
期刊:
影响因子:
5
通讯作者:
Qi, Xue
Qi, Xue
中科院分区:
工程技术2区
文献类型:
--
作者:
Zhang, Li-Jun;Pang, Yong-Jie;Qi, Xue

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研究了六自由度AUV的跟踪控制问题。一般情况下,车辆的速度很难精确测量,这导致全状态反馈方案是不可行的。因此,提出了一种基于动态递归模糊神经网络(DRFNN)的自适应输出反馈控制器,该控制器的设计只需要位置信息。DRFNN用于动态不确定非线性映射的在线估计。与传统的神经网络相比,DRFNN具有输入量少、记忆能力强等优点,可以明显提高AUV的跟踪性能。本文打破了现有文献中经常给出的外部扰动和网络逼近误差估计的限制条件。利用李雅普诺夫定理对系统的稳定性进行了分析。仿真结果表明了所提出的控制方案的有效性。
The tracking control problem of AUV in six degrees-of-freedom (DOF) is addressed in this paper. In general, the velocities of the vehicles are very difficult to be accurately measured, which causes full state feedback scheme to be not feasible. Hence, an adaptive output feedback controller based on dynamic recurrent fuzzy neural network (DRFNN) is proposed, in which the location information is only needed for controller design. The DRFNN is used to online estimate the dynamic uncertain nonlinear mapping. Compared to the conventional neural network, DRFNN can clearly improve the tracking performance of AUV due to its less inputs and stronger memory features. The restricting condition for the estimation of the external disturbances and network's approximation errors, which is often given in the existing literatures, is broken in this paper. The stability analysis is given by Lyapunov theorem. Simulations illustrate the effectiveness of the proposed control scheme.
DOI: 10.1109/oceans.2005.1640019
发表时间: 2005-09
期刊: Proceedings of OCEANS 2005 MTS/IEEE
影响因子: --
作者:
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