Tracking Control for Robot Manipulator Based on Deterministic Learning and Event-Triggered Mechanism
作者单位：School of Automation Science and Engineering South China University of Technology School of Medicine South China University of Technology
会议名称：《第三十九届中国控制会议》
会议日期：2020年
学科分类：08[工学] 080202[工学-机械电子工程] 0802[工学-机械工程]
关 键 词：Deterministic learning event-triggered mechanism adptive neural control robot manipulator
摘 要：In this paper, a control scheme is presented for robotic manipulator by combining deterministic learning and eventtriggered mechanism. Notice that the traditional adaptive neural control needs to re-adapt neural network（NN） weights even for performing the same control task, thereby result in the limited learning ability. To enhance the neural leaning ability, a dynamic learning controller is firstly constructed based on the radial basis function（RBF） NN, which has the ability to learn the knowledge of the unknown closed-loop system dynamics. By reusing the learned knowledge, the dynamic learning-based event-triggered control scheme is put forward to achieve a tradeoff between the network resource and the tracking performance. Specially, an easy-to-implemented event-triggered condition is designed by the Lyapunov technique due to the use of experience knowledge.The proposed scheme ensures that the tracking error converges to a small neighborhood of the origin, all the signals in the closedloop system are bounded and meanwhile the communication resources are greatly reduced. A comparison simulation example is conducted to demonstrate the effectiveness and advantage of the proposed control scheme.