Adaptive Boundary Vibration Control and Angle Tracking Consensus of Networked Flexible Timoshenko Manipulator Systems
研究了参数不确定和外部干扰下网络化柔性铁木辛柯机械臂系统的自适应边界振动控制与角度跟踪一致性问题，提出了分布式自适应连续和事件触发边界控制算法，并通过仿真验证了可行性。
The adaptive boundary vibration control and leader–follower angle tracking consensus problem of networked flexible Timoshenko manipulator systems in the case of parametric uncertainties and external disturbances are investigated. To mitigate the impacts of external disturbances and parameter uncertainties, we employ the adaptive tuning techniques to design some suitable adaptive terms. Then with network topology is connected, we propose some novel distributed adaptive continuous-time boundary control algorithms. Based on a novel combination of Lyapunov-based control and Barbalat’s lemma, the asymptotic control performances can be achieved. Besides, some novel distributed adaptive event-triggered boundary control algorithms are also proposed for the undisturbed Timoshenko manipulators. The advantage of the considered event-triggered algorithms is that they can avoid continuous updates of the controller and thereby can effectively save control resources. Moreover, Zeno behaviors are excluded for all agents to ensure the practicality of digital sampling. Finally, we illustrate the feasibility of the proposed control algorithms through numerical simulation examples.