• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Quantum-Interference Artificial Neural Network With Application to Space Manipulator Control
【24h】

Quantum-Interference Artificial Neural Network With Application to Space Manipulator Control

机译：量子干扰人工神经网络应用于太空机械手控制

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Since advanced controllers for space manipulators rely heavily on the accuracy of the dynamic model and state feedback signals, neural-network-based adaptation approaches are often adopted to provide the robustness of the control system. In order to improve the performance of existing neural-network-based adaptive controllers, a new artificial neural network framework is presented based on the quantum-interference principle. A new activation function is established by quantum interference to fulfill the requirement of being a universal approximator. Driven by this new activation function, the classic Delta training method is replaced by an optimal on-line training rule to ensure better performance at a higher training rate (TR), which makes the new neural network more capable of tracking high-frequency noises. The quantum-interference neural network is then integrated into the space manipulator adaptive controller to track the estimation error of the model parameters and disturbances. The advantage of the new neural network at a high TR is validated by simulations, which shows a promising solution to the error tracking and compensation control for space manipulators.
机译：由于空间操纵器的高级控制器严重依赖于动态模型和状态反馈信号的准确性，因此通常采用基于神经网络的适应方法来提供控制系统的鲁棒性。为了提高现有神经网络的自适应控制器的性能，基于量子干扰原理提出了一种新的人工神经网络框架。量子干扰建立了一种新的激活功能，以满足是通用近似器的要求。通过这种新的激活功能驱动，经典的Delta训练方法被最佳的在线训练规则所取代，以确保以更高的训练率（TR）更好的性能，这使得新的神经网络更能够跟踪高频噪声。然后将量子干扰神经网络集成到空间操纵器自适应控制器中，以跟踪模型参数和干扰的估计误差。通过仿真验证了高TR处的新神经网络的优点，该模拟显示了对空间操纵器的误差跟踪和补偿控制的有希望的解决方案。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号