...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechanical systems and signal processing >A neuroadaptive control method for pneumatic artificial muscle systems with hardware experiments
【24h】

A neuroadaptive control method for pneumatic artificial muscle systems with hardware experiments

机译：硬件实验充气人工肌肉系统的神经视觉控制方法

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

Pneumatic artificial muscle (PAM) actuators are a kind of biomimetic actuators, which are being widely used in the applications of biomimetic robots and medical auxiliary devices. However, PAM systems usually have high nonlinearities, uncertainties, and time-varying characteristics, which bring challenges for accurate dynamic modeling and controller design. To deal with the above issues, in this paper, a neuroadaptive control method is proposed to handle the system uncertainties and achieve satisfactory tracking performance. First, in order to compensate the unknown nonlinear term involved in the dynamic model of the PAM system online, a three-layer neural network is utilized. Next, by means of the filtered signal, the algebraic loop problem can be solved effectively. Then, based on a sliding mode surface, a nonlinear robust controller is designed. By using the proposed method, the asymptotic convergence of tracking errors of the PAM system is guaranteed, and the tracking errors are always restricted within preset bounds during the control process. Moreover, the stability of the closed-loop system is proven theoretically by utilizing Lyapunov techniques. Finally, a series of hardware experiments are implemented on a self-built PAM testbed to validate the effectiveness and robustness of the proposed neuroadaptive control method.
机译：气动人造肌肉（PAM）致动器是一种仿生致动器，其被广泛用于仿生机器人和医用辅助装置的应用。然而，PAM系统通常具有高的非线性，不确定性和时变特性，这带来了准确的动态建模和控制器设计的挑战。为了处理上述问题，本文提出了一种神经直视控制方法来处理系统不确定性并实现令人满意的跟踪性能。首先，为了补偿在线PAM系统的动态模型中涉及的未知非线性术语，利用了一种三层神经网络。接下来，借助于滤波信号，可以有效地解决代数环问题。然后，基于滑动模式表面，设计了非线性鲁棒控制器。通过使用所提出的方法，保证了PAM系统的跟踪误差的渐近收敛，并且在控制过程期间总是限制在预设边界内的跟踪误差。此外，通过利用Lyapunov技术理论上证明了闭环系统的稳定性。最后，一系列硬件实验在自制的PAM测试台上实施，以验证所提出的神经直观控制方法的有效性和稳健性。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2021年第1期|106976.1-106976.15|共15页
  • 作者

    Yiheng Chen; Ning Sun; Dingkun Liang; Yanding Qin; Yongchun Fang;

  • 作者单位

    Institute of Robotics and Automatic Information Systems (IRAIS) Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics (tjKLIR) Nankai University Tianjin China;

    Institute of Robotics and Automatic Information Systems (IRAIS) Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics (tjKLIR) Nankai University Tianjin China;

    Institute of Robotics and Automatic Information Systems (IRAIS) Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics (tjKLIR) Nankai University Tianjin China;

    Institute of Robotics and Automatic Information Systems (IRAIS) Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics (tjKLIR) Nankai University Tianjin China;

    Institute of Robotics and Automatic Information Systems (IRAIS) Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics (tjKLIR) Nankai University Tianjin China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Pneumatic artificial muscle (PAM); Neural network (NN); Neuroadaptive control;

    机译：气动人工肌肉（PAM）;神经网络（NN）;神经直视控制;

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