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A novel fuzzy B-spline neural network and applied into mobile satcom antenna

机译:一种新颖的模糊B样条神经网络并应用于移动卫星通信天线

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This work proposed a novel fuzzy B-Spline neural network (FBSNN), and applied to the control system of mobile satcom antenna (MSA). To communicate continuously, the antenna must always points to the desired satellite during vehicle motion. Therefore, the gimbals pointing must be controlled accurately. The control block diagram is established based on the dynamic model. Due to disturbance and coupling effects that exist in system, adaptive control strategy should be adopted. The proposed FBSNN is applied to approximate nonlinear element and disturbance. Compared with conventional FBSNN, the proposed method not only adjust the output weights, but also has ability to adjust shapes and positions of membership function. The adjustment method is obtained by reconstruction B-Spline function, and the training method is also derived based on the property of B-Spline. Simulations show that the proposed method performs better than fuzzy radial basis function (RBF) neural network and conventional PID control method.
机译:这项工作提出了一种新型模糊B样条神经网络(FBSNN),并应用于移动卫星天线(MSA)的控制系统。为了连续沟通,天线必须始终指向车辆运动期间所需的卫星。因此,必须准确控制指向的GIMBALS指向。基于动态模型建立控制框图。由于系统中存在的扰动和耦合效果,应采用自适应控制策略。所提出的FBSNN应用于近似非线性元素和干扰。与传统的FBSNN相比,所提出的方法不仅调整输出权重,还可以调整隶属函数的形状和位置。调整方法是通过重建B样条函数获得的,并且还基于B样条的特性导出训练方法。模拟表明,该方法比模糊径向基函数(RBF)神经网络和传统的PID控制方法更好。

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