Event-Triggered Prescribed-Time Fuzzy Control for Space Teleoperation Systems Subject to Multiple Constraints and Uncertainties

遥操作 控制理论(社会学) 计算机科学 模糊逻辑 控制器(灌溉) 区间(图论) 模糊控制系统 弹道 沉降时间 运动学 数学优化 机器人 数学 控制工程 控制(管理) 人工智能 工程类 组合数学 阶跃响应 物理 生物 经典力学 农学 天文
作者
Ziwei Wang,Hak‐Keung Lam,Bo Xiao,Zhang Chen,Bin Liang,Tao Zhang
出处
期刊:IEEE Transactions on Fuzzy Systems [Institute of Electrical and Electronics Engineers]
卷期号:29 (9): 2785-2797 被引量:75
标识
DOI:10.1109/tfuzz.2020.3007438
摘要

Limited by the operation time window and working space, space teleoperation tasks need to be completed within an expected time while ensuring that the end effector meets the physical constraints. Meanwhile, the interaction with unknown environments would cause uncertainty in the closed-loop system, which brings great challenges to the control design. To solve the above problems, the control performance issue for a class of space teleoperation systems subject to multiple constraints and interaction uncertainties is investigated in this article. The force interaction with the human operator/space environment is represented by interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems, where the uncertain equivalent mass and damping parameters can be effectively described and captured by IT2 membership functions. In order to reduce the communication burden and satisfy the constraints of settling time, transient-state performance and operating space, a time-varying threshold event-triggered control scheme together with exponential-type Lyapunov function is developed for the first time. We show that, with the proposed controller, the synchronization tracking errors are guaranteed to converge to a user-defined residual set within preassigned settling time, and never exceed the prescribed range despite unknown control direction and actuator faults, which solves the long-standing constraint issue with more flexibility due to the fact that the related constraints can be arbitrarily specific within the physically available range. Moreover, the convergence set is only dependent on fewer user-defined parameters rather than approximation errors, which provides an effective analysis technique to deal with the difficulty that the convergence accuracy is difficult to calculate quantitatively in the presence of unknown disturbance. Detailed simulation results are provided to show the effectiveness and merit of the proposed control strategy.
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