网络化遥操作系统的全状态时变约束控制研究  

Research on full-state time-varying constraints control for networked teleoperation systems

作　　者：杨亚娜 代特 YANG Yana;DAI Te(School of Electrical Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China)

机构地区：[1]燕山大学电气工程学院,河北秦皇岛066004

出　　处：《燕山大学学报》2021年第4期357-366,共10页Journal of Yanshan University

基　　金：国家自然科学基金资助项目(61933009,61703361);河北省教育厅拔尖人才资助项目(BJ2019047)。

摘　　要：本文针对全状态时变约束下的遥操作系统,在系统不确定和非对称时变时滞下研究了主-从系统之间的同步控制问题。为提高主-从系统同步精度,避免因系统状态突然增大导致的碰撞问题,本文创新性地将系统的全状态时变约束问题转化为系统的稳定性问题。同时,针对主-从系统之间的不对称时变时延,通过引入新的非线性观测器来保证闭环遥操作系统的稳定性。进而,引入了径向基神经网络来在线估计系统动态不确定项,并应用Nussbaum增益处理系统参数不确定,保证了系统的零误差追踪。通过构造新的障碍Lyapunov函数证明了主-从系统的稳定性和同步性能。最后,仿真实验验证了所设计控制算法的有效性。In this paper,the problem of synchronous control with system uncertainty and asymmetric time-varying delays between master-slave systems is studied for teleoperation system with all-state constraints.This study innovatively transforms the system′s full-state time-varying constraint problem into a system stability problem to improve the synchronization accuracy of the master-slave system and avoid the collision problem caused by the sudden increase of the system.At the same time,in view of the asymmetric time-varying delay between the master-slave system,a new nonlinear observer is introduced to ensure the stability of the closed-loop teleoperation system.Furthermore,a radial basis function neural network is introduced to estimate the system dynamic uncertainty online,and Nussbaum gain is used to deal with the system parameter uncertainty,which ensures the zero error tracking of the system.The stability and synchronization performance of the master-slave system are proved by constructing a new obstacle Lyapunov function.Finally,simulation results verify the effectiveness of the proposed control algorithm.

关 键 词：网络化遥操作控制系统 障碍Lyapunov函数 全状态约束 NUSSBAUM增益 时变时滞 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]