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Research on ship slanting rudder anti-pitching intelligent adaptive Generalized Predictive Control

机译:船舶斜舵防倾角智能自适应广义预测控制研究

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This article presents a method to build a T-S fuzzy model based on Generalized Dynamic Fuzzy Neural Network (GD-FNN) of Elliptical Basis Function in order to solve the problem of ship motion model's uncertainty and nonlinearity. The proposed method needs neither prior fuzzy neural networks structure knowledge nor prior training phase, it can be used to build the nonlinear and uncertain part through online adaptive learning algorithm. The fuzzy rules could be generated and pruned on-line by learning. The ship vertical (heave and pitch) dynamic linear adaptive CARMA model can be got by local dynamic linearization at each sampling point. Then Generalized Predictive Control (GPC) law is deduced by combining adaptive linear model with generalized predictive control. Simulation experiment shows that this algorithm is effective and efficient, its anti-pitching effect reaches 82.2%.
机译:本文提出了一种基于椭圆基函数的广义动态模糊神经网络(GD-FNN)建立T-S模糊模型的方法,以解决船舶运动模型的不确定性和非线性问题。该方法既不需要先验模糊神经网络的结构知识,也不需要先验训练阶段,就可以通过在线自适应学习算法来构建非线性和不确定部分。模糊规则可以通过学习在线生成和修剪。船舶垂直(升沉和俯仰)动态线性自适应CARMA模型可以通过在每个采​​样点进行局部动态线性化来获得。然后通过将自适应线性模型与广义预测控制相结合来推导广义预测控制(GPC)定律。仿真实验表明,该算法是有效且高效的,其抗音调效果达到了82.2%。

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