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Positioning controller of a remotely operated vehicle using RWNN-based adaptive robust controllers

机译:使用基于RWNN的自适应鲁棒控制器的遥控车辆的定位控制器

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In this paper, a robust adaptive controller is designed for the dynamical positioning of a remotely operated vehicle (ROV) with unknown hydrodynamic coefficients and disturbances. Firstly, the Multiple Input Multiple Output (MIMO) system can be divided into several Single Input Single Output (SISO) systems for convenience of controller design. Then, a recurrent wavelet neural network (RWNN) with feedback loops is employed to approach the unknown part of the controller, and it can greatly reduce the number of neural network's inputs. Though the upper bounds of the network's approximation errors and disturbances are often used in the existed work, they are not necessary in this paper by presenting a special robust compensator. The stability analysis is given by Lyapunov theorem, and the tracking errors are proved to convergence to zero. Simulation result are presented for the ROV's positioning system, which indicate that the proposed controller has a good performance.
机译:在本文中,设计了一种鲁棒的自适应控制器,用于对具有未知流体动力系数和扰动的遥控车辆(ROV)进行动态定位。首先,为了方便控制​​器设计,可以将多输入多输出(MIMO)系统分为几个单输入单输出(SISO)系统。然后,采用带有反馈回路的递归小波神经网络(RWNN)来接近控制器的未知部分,它可以大大减少神经网络的输入数量。尽管在现有工作中经常使用网络逼近误差和扰动的上限,但在本文中,通过提出一种特殊的鲁棒补偿器,并不需要它们的上限。利用李雅普诺夫定理给出稳定性分析,证明跟踪误差收敛到零。给出了机器人定位系统的仿真结果,表明所提出的控制器具有良好的性能。

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