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首页> 外文期刊>Advances in Mechanical Engineering >Neural adaptive sliding mode controller for unmanned surface vehicle steering system
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Neural adaptive sliding mode controller for unmanned surface vehicle steering system

机译：用于无人地面车辆转向系统的神经自适应滑模控制器

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Unmanned surface vehicle has the properties such as complexity, nonlinearity, time variability, and uncertainty, which lead to the difficulty of obtaining a precise kinematics model. A neural adaptive sliding mode controller for the unmanned surface vehicle steering system is developed based on the sliding mode control technique and the radial basis function neural network. In the new approach, two parallel radial basis function neural networks are used to reduce the influence of the system uncertainties and eliminate the dependency of the controller on the precise kinematics model of the system. Among these two radial basis function neural networks, one is used to approximate the unknown nonlinear yaw dynamics and the other is used to adjust the control gain as well as realize the variable gain sliding mode control. The weights of the two neural networks are trained online using the sliding surface variable and the control, where the Lyapunov method is used to derive the adaptive laws to ensure the stabi.
机译：无人水面车辆具有诸如复杂性，非线性，时间可变性和不确定性之类的特性，这导致难以获得精确的运动学模型。基于滑模控制技术和径向基函数神经网络，开发了无人水面转向系统的神经自适应滑模控制器。在新方法中，使用两个并行的径向基函数神经网络来减少系统不确定性的影响，并消除控制器对系统精确运动学模型的依赖性。在这两个径向基函数神经网络中，一个用于逼近未知的非线性偏航动力学，另一个用于调节控制增益并实现可变增益滑模控制。使用滑动表面变量和控件在线训练两个神经网络的权重，其中使用Lyapunov方法导出自适应定律以确保稳定。

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