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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定位远程操作车辆的控制器

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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:本文设计了一种坚固的自适应控制器,用于具有未知的流体动力系数和干扰的远程操作车辆(ROV)的动态定位。首先,为方便控制器设计,多输入多输出(MIMO)系统可分为几个输入单输出(SISO)系统。然后,采用具有反馈回路的反复间小波神经网络(RWNN)来接近控制器的未知部分,并且可以大大减少神经网络的输入的数量。尽管网络的近似误差和干扰的上限通常在存在的工作中使用,但是本文不需要通过呈现特殊的鲁棒补偿器。稳定性分析由Lyapunov定理提供,并证明跟踪误差会收敛到零。仿真结果显示为ROV的定位系统,这表明所提出的控制器具有良好的性能。

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