基于神经网络的六自由度摇摆台位置正解
Forward Kinematics Solution of Stewart Platform Based on Neural Networks
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摘要: 研究用多层前向神经网络求解六自由度摇摆台位置正解的方法.通过位置反解产生神经网络的学习样本,并进行数据预处理.针对位置正解映射关系的复杂性,确立对应于摇摆台6个自由度的6个子神经网络作为位置正解的网络模型.提出用重构学习样本的方法改善网络误差分布的不利特点.仿真结果表明,用神经网络求解六自由度摇摆台的位置正解是有效的;摇摆台在全工作范围内,神经网络位置正解的误差为0.5°,3mm.Abstract: A method based on the neural network for the forward kinematics solution of Stewart platform is studied. The training examples are obtained through the inverse kinematics, and then preprocessed. The final neural network includes six sub-networks, corresponding to the six degrees-of-freedom. Unfavorable error distributions in the network are improved through re-construction of the training examples. The simulation results show that the neural network operates well and provides the forward kinematics solution an error of 0.5° and 3 mm when the platform runs in its full range.
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