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Nonlinear adaptive control synthesis using U-model for multivariable underwater remotely operated vehicle

机译:非线性自适应控制合成使用U形模型进行多变化水下遥控车辆

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This paper presents the development of ROV control modelling and control synthesis using nonlinear adaptive U-model approach. Nonlinear ROV model based on the dynamic equation using the Newtonian method and derivation towards the kinematics equations and rigid-body mass matrixes are explained. This nonlinear ROV model represents the underwater thruster dynamics, ROV dynamics and kinematics related to the earth-fixed frame. MIMO Nonlinear adaptive control synthesis using U-model approach incorporate with neural networks algorithm are developed with MATLAB? Simulink software and integrated together with the nonlinear ROV model using Internal Model Control structure. The controller output is based on Newton Raphson recursive algorithm with learning rate value between zero and one which improve the system stability. Radial basis function (RBF) is chosen for the neural networks activation function due to faster learning speed. Results show good control signal convergence and tracking performance between plant or system model with U-model polynomial.
机译:本文介绍了使用非线性自适应U形模型方法的ROV控制建模和控制合成的发展。解释了基于使用牛顿方法的动态方程的非线性ROV模型,并朝向运动学方程衍生和刚体质量矩阵。该非线性ROV模型代表水下推进器动力学,ROV动力学和与地球固定框架相关的运动学。使用MATLAB开发了使用U模型方法与神经网络算法合并的MIMO非线性自适应控制合成? Simulink软件并使用内部模型控制结构与非线性ROV模型集成。控制器输出基于Newton Raphson递归算法,其学习速率值在零和一个提高系统稳定性之间。由于更快的学习速度,为神经网络激活功能选择了径向基函数(RBF)。结果显示良好的控制信号收敛性和U型多项式植物或系统模型之间的跟踪性能。

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