School of Automation, Guangdong University of Technology, Guangzhou, 510006, China.
School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, 510006, China.
Neural Netw. 2023 Sep;166:1-10. doi: 10.1016/j.neunet.2023.06.032. Epub 2023 Jul 4.
In this paper, an adaptive prescribed settling time periodic event-triggered control (APST-PETC) is investigated for uncertain robotic manipulators with state constraints. In order to economize network bandwidth occupancy and reduce computational burden, a periodic event-triggered control (PETC) strategy is proposed to reduce the update frequency of the control signal and avoid unnecessary continuous monitoring. Besides, considering that the maneuverable space of the actual robotic manipulators is often limited, the barrier Lyapunov function (BLF) is applied to deal with the influence of the constraint characteristics on the robotic manipulators. Further, based on the one-to-one nonlinear mapping function of the system tracking error, an adaptive prescribed settling time control (APSTC) is designed to ensure that the system tracking error reaches the predetermined precision residual set within the prescribed settling time. Finally, theoretical analysis and comparative experiments are given to verify its feasibility.
本文针对具有状态约束的不确定机器人机械手，研究了一种自适应规定调整时间周期事件触发控制（APST-PETC）。为了节省网络带宽占用和降低计算负担，提出了一种周期性事件触发控制（PETC）策略，以降低控制信号的更新频率，避免不必要的连续监测。此外，考虑到实际机器人机械手的可操纵空间通常是有限的，应用障碍李雅普诺夫函数（BLF）来处理约束特性对机器人机械手的影响。进一步地，基于系统跟踪误差的一一对应非线性映射函数，设计了一种自适应规定调整时间控制（APSTC），以确保系统跟踪误差在规定的调整时间内达到预定的精度残差集。最后，给出了理论分析和对比实验，以验证其可行性。
