【讲座题目】Reciprocal of Exponential Varying-Parameter RNN Solving Repetitive Tracking Control Problems with Tolerance of Random Initial Error Compounded with Noise Perturbation
【主讲人】刘万泉教授
【讲座时间】2023年06月15日星期四 上午10:00-11:00
【讲座地点】腾讯会议ID：540-621-552
【主讲人简介】Prof. Dr. Wanquan Liu received the BSc degree in Applied Mathematics from Qufu Normal University, P. R. China, in 1985, the MSc degree in Control Theory and Operation Research from Chinese Academy of Science in 1988, and the PhD degree in Electrical Engineering from Shanghai Jiaotong University, in 1993. He once held the ARC Fellowship, U2000 Fellowship and JSPS Fellowship and attracted research funds from different resources over 4.0 million Australian dollars. He is currently a Professor in the School of Intelligent Systems Engineeringat Sun Yat-sen University and is the Editor-in-chief for the international journal Mathematical Foundation of Computing and in editorial board for several international journals. His current research interests include intelligent control systems, pattern recognition, machine learning, and computer vision.
【讲座内容简介】Positioning, posture of the robotic joints and end-effector could probably introduce random initial errors. Those errors could exponentially deteriorate with compounded of common noise perturbation to cause the final failure of repetitive tracking control. To better improve the tolerance of those complex errors, a novel reciprocal of exponential varying-parameter recurrent neural network (RE-VP-RNN) is proposed to consider superimposed noise interference including the initial position deviation and noise perturbation together. Theoretical analysis further proves the convergence of the proposed method. The effectiveness, accuracy, and robustness of the proposed RE-VP-RNN solver are verified by simulation and physical experiments on three representative redundant and hype-redundant manipulators. The proposed model could be widely used in robot control for high-precision machining scenarios such as medical, industry, and aviation.
