KMS Chongqing Institute of Green and Intelligent Technology, CAS
Dynamic Neural Network for Bicriteria Weighted Control of Robot Manipulators | |
Liu, Mei1,2; He, Li1,2; Shang, Mingsheng2 | |
2021-10-07 | |
摘要 | In recent years, bicriteria optimization schemes for manipulator control have become preferred by researchers, given their satisfactory performance. In this article, a bicriteria weighted (BCW) scheme to remedy joint drift and minimize the infinity norm of joint velocity is proposed. The scheme adopts a novel repetitive motion index that can theoretically decouple the joint error and the position error, which many conventional cyclic motion generation schemes cannot achieve. Subsequently, through transformation, the BCW scheme is converted into a time-varying quadratic programming (QP) problem. Then, a dynamic neural network (DNN) system with a new Fisher-Burmeister function is proposed to address the resulting QP problem. It is proven that the proposed DNN system is free of residual errors, which means that the actual solution is able to converge to the theoretical solution. Another essential feature of the DNN system is that it has a suppression effect on noise. To demonstrate the convergence and robustness of the proposed DNN system, comparative simulations are carried out in nominal and noisy environments. Finally, experiments on Franka Emika Panda are conducted to elucidate the availability of the BCW scheme addressed by the DNN system. |
关键词 | Manipulators Robots Indexes Mathematical models Manipulator dynamics Optimization Kinematics Bicriteria weighted (BCW) scheme dynamic neural network (DNN) quadratic programming (QP) problem robustness |
DOI | 10.1109/TNNLS.2021.3116321 |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
页码 | 14 |
通讯作者 | Shang, Mingsheng(msshang@cigit.ac.cn) |
收录类别 | SCI |
WOS记录号 | WOS:000732351500001 |
语种 | 英语 |