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Event-Based Variance-Constrained ${\mathcal {H}}_{\infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements
IEEE Transactions on Cybernetics ( IF 9.4 ) Pub Date : 2017-03-06 , DOI: 10.1109/tcyb.2017.2671032
Licheng Wang , Zidong Wang , Qing-Long Han , Guoliang Wei

Due to the cognitive limitations of the human operator and lack of complete information about the remote environment, the work performance of such teleoperation systems cannot be guaranteed in most cases. However, some practical tasks conducted by the teleoperation system require high performances, such as tele-surgery needs satisfactory high speed and more precision control results to guarantee patient’ health status. To obtain some satisfactory performances, the error constrained control is employed by applying the barrier Lyapunov function (BLF). With the constrained synchronization errors, some high performances, such as, high convergence speed, small overshoot, and an arbitrarily predefined small residual constrained synchronization error can be achieved simultaneously. Nevertheless, like many classical control schemes only the asymptotic/exponential convergence, i.e., the synchronization errors converge to zero as time goes infinity can be achieved with the error constrained control. It is clear that finite time convergence is more desirable. To obtain a finite-time synchronization performance, the terminal sliding mode (TSM)-based finite time control method is developed for teleoperation system with position error constrained in this paper. First, a new nonsingular fast terminal sliding mode (NFTSM) surface with new transformed synchronization errors is proposed. Second, adaptive neural network system is applied for dealing with the system uncertainties and the external disturbances. Third, the BLF is applied to prove the stability and the nonviolation of the synchronization errors constraints. Finally, some comparisons are conducted in simulation and experiment results are also presented to show the effectiveness of the proposed method.

中文翻译:


基于事件的方差约束 ${\mathcal {H}}_{\infty }$ 对连续丢失测量的传感器网络上的随机参数系统进行过滤



由于操作人员的认知限制以及缺乏远程环境的完整信息,大多数情况下此类遥操作系统的工作性能无法得到保证。然而,远程操作系统执行的一些实际任务需要高性能,例如远程手术需要令人满意的高速和更精确的控制结果以保证患者的健康状态。为了获得一些令人满意的性能,通过应用势垒李亚普诺夫函数(BLF)来采用误差约束控制。通过约束同步误差,可以同时实现一些高性能,例如高收敛速度、小超调以及任意预定义的小残余约束同步误差。然而,与许多经典控制方案一样,误差约束控制只能实现渐近/指数收敛,即随着时间无限远同步误差收敛到零。显然,有限时间收敛更可取。为了获得有限时间同步性能,本文针对位置误差受限的遥操作系统开发了基于终端滑模(TSM)的有限时间控制方法。首先,提出了一种具有新变换同步误差的新非奇异快速终端滑模(NFTSM)表面。其次,应用自适应神经网络系统来处理系统的不确定性和外部干扰。第三,应用BLF来证明稳定性和不违反同步误差约束。最后,通过仿真进行了比较,并给出了实验结果,证明了该方法的有效性。
更新日期:2017-03-06
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