共查询到18条相似文献,搜索用时 62 毫秒
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针对小型自主水下航行器(AUV)尾舵设计的关键问题,采用二维计算流体力学(CFD)方法模拟了流速为2m/s时.常用NACA00××系列翼型在不同来流下的情况,得到了小型自主水下航行器常用翼型的水动力特性;采用DNV规范计算得到了尾舵面积的大小;采用三维CFD方法研究了舵轴位于不同位置时的铰链力矩.找到了具有最小铰链力矩的舵轴位置.文中的研究结果为小型水下自航行器尾舵的设计提供了理论依据和参考. 相似文献
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研究自主水下航行器系统的软变结构控制策略问题。首先分析软变结构控制系统的结构特征,利用双曲正切函数,给出控制受限情形的软变结构控制策略。其次利用Lyapunov稳定性理论,讨论自主水下航行器软变结构控制系统的稳定性,然后构造了基于双曲正切函数的软变结构控制器,给出自主水下航行器软变结构控制的具体算法。基于双曲正切函数的自主水下航行器软变结构控制系统调节精度高,响应速度快,有效地削弱了系统抖振。最后通过一个仿真实验,比较了自主水下航行器垂直深度通道的4种控制策略对系统性能的影响,从而验证了研究方法的有效性。 相似文献
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The stability of the motion control system is one of the decisive factors of the control quality for Autonomous Underwater Vehicle (AUV).The divergence of control,which the unstable system may be brought about,is fatal to the operation of AUV.The stability analysis of the PD and S-surface speed controllers based on the Lyapunov' s direct method is proposed in this paper.After decoupling the six degree-of-freedom (DOF) motions of the AUV,the axial dynamic behavior is discussed and the condition is deduced,in which the parameters selection within stability domain can guarantee the system asymptotically stable.The experimental results in a tank and on the sea have successfully verified the algorithm reliability,which can be served as a good reference for analyzing other AUV nonlinear control systems. 相似文献
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针对水下机器人操纵性优化设计中水动力系数预报问题,在水下机器人水动力预报中引入艇体肥瘦指数概念,确定了水下机器人艇体几何描述的五参数模型。提出采用小波神经网络方法预报水下机器人水动力,确定了神经网络的结构,利用均匀试验设计方法,设计了神经网络的学习样本。研究结果表明,只要确定适当的输入参数,选择适当的学习样本和网络结构,利用小波神经网络方法对水下机器人水动力进行预报可以达到较好的精度。 相似文献
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A parallel neural network-based controller (PNNC) is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller' s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles. 相似文献
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A robust neural network controller (NNC) is presented for tracking control of underwater vehicles with uncertainties. The controller is obtained by using backstepping technique and Lyapunov function design in combination with neural network identification. Modeling errors and environmental disturbances are considered in the mathematical model. A two-layer neural network is introduced to compensate the modeling errors, while H∞ control strategy is used to achieve the L2-gain performance. The uniformly ultimately bounded (UUB) stabilities of tracking errors and NN weights are guaranteed through the proposed controller. An on-line NN weights tuning algorithm is also proposed. Good performances of the tracking control system are illustrated by the results of numerical simulations. 相似文献