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Mode Switching-Based Symmetric Predictive Control Mechanism for Networked Teleoperation Space Robot System

机译:网络遥操作空间机器人系统基于模式切换的对称预测控制机制

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摘要

Bilateral synchronization control problem for networked teleoperation space robot system (NTSRS) with unknown gravity and asymmetric, random delay as well as communication intermittent interruption is discussed in this paper. To make full use of limited operation window time, improve telepresence, and realize continuous teleoperation, a special mode switching-based symmetric predictive control mechanism is designed, where the mode switching idea is to make full use of limited operation time. Meanwhile, among this mechanism, the symmetric predictive control structure is to improve telepresence and realize continuous teleoperation, besides the same function that mode switching idea provides. The structure of this mechanism for the master side is composed of a predictor and corresponding controller, and symmetric configurations for slave side. Combining with the homomorphic model idea and utilizing the predictive and estimation capability of neural network (NN), novel predictor design method is proposed. In designing the predictor, unlike the traditional model predictive control where predictor is coupled with the controller, this method is independent with the corresponding controller in structure, which is helpful for parameter adjustment and analysis. For unknown gravity, estimation ability of NN is utilized to solve it. With the adaptive method, estimation errors can be kept at a lower level. Subsequently, an analogous proportional-derivative controller with gravity compensation term is designed to realize asymptotical stability and synchronization control of the NTSRS. Finally, effectiveness of the proposed method is verified by some simulation examples.
机译:本文讨论了重力未知,不对称,随机延迟以及通信间歇中断的网络遥距太空机器人系统(NTSRS)的双向同步控制问题。为了充分利用有限的操作窗口时间,提高远程呈现能力,实现连续的远程操作,设计了一种特殊的基于模式切换的对称预测控制机制,其中模式切换的思想是充分利用有限的操作时间。同时,在这种机制中,对称预测控制结构除了提高模式切换思想所提供的功能外,还可以提高远程呈现并实现连续远程操作。主侧的此机制的结构由预测器和相应的控制器以及从侧的对称配置组成。结合同态模型思想,利用神经网络的预测和估计能力,提出了一种新颖的预测器设计方法。在设计预测器时,不同于将预测器与控制器耦合的传统模型预测控制,该方法在结构上独立于相应的控制器,这有助于参数调整和分析。对于未知重力,利用神经网络的估计能力来解决。利用自适应方法,可以将估计误差保持在较低水平。随后,设计了具有重力补偿项的比例微分控制器,以实现NTSRS的渐近稳定性和同步控制。最后,通过仿真实例验证了该方法的有效性。

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