首页> 外文期刊>Soft Computing - A Fusion of Foundations, Methodologies and Applications >Robust path tracking control of mobile robot via dynamic petri recurrent fuzzy neural network
【24h】

Robust path tracking control of mobile robot via dynamic petri recurrent fuzzy neural network

机译:动态Petri递归模糊神经网络的移动机器人鲁棒路径跟踪控制

获取原文
获取原文并翻译 | 示例

摘要

This study focuses on the design of robust path tracking control for a mobile robot via a dynamic Petri recurrent fuzzy neural network (DPRFNN). In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the dynamic mapping of network ability. This five-layer DPRFNN is utilized for the major role in the proposed control scheme, and the corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance without the requirement of detailed system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed robust DPRFNN control scheme is verified by experimental results of a differential-driving mobile robot under different moving paths and the occurrence of uncertainties, and its superiority is indicated in comparison with a stabilizing control system.
机译:这项研究致力于通过动态Petri递归模糊神经网络(DPRFNN)设计移动机器人的鲁棒路径跟踪控制。在DPRFNN中,将Petri网(PN)的概念和内部反馈回路的递归框架合并到传统的模糊神经网络(FNN)中,以减轻参数学习的计算负担并增强网络能力的动态映射。该五层DPRFNN用于所提出的控制方案中的主要作用,并根据投影算法和Lyapunov稳定性定理建立了相应的网络参数自适应律,以确保网络收敛和稳定的控制性能,而无需控制算法。详细的系统信息要求和辅助控制器的补偿。此外,通过在不同运动路径和不确定性条件下的差动驱动移动机器人的实验结果验证了所提出的鲁棒DPRFNN控制方案的有效性,并且与稳定控制系统相比,它的优越性得到了证明。

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