A more general incremental inter-agent learning adaptive control for multiple identical processes in mass production
作者： Hongyi Qu , Dewei Li , Ridong Zhang , Shuang-Hua Yang , Furong Gao
DOI: 10.1016/J.NEUCOM.2020.02.021
关键词:
摘要: Abstract Mass production has been a trend for modern manufacturing. Information from peer processes may be used to improve the individual process control performance. The idea of ‘incremental step mimicking’ presented in our previous work [1] which resulted inter-agent learning’ (IIAL) adaptive control. However, that is based on primitive input directly calculated online-identified model. This paper explores learning more complicated control, and proposes general Full-Scale IIAL (FS-IIAL) can viewed as special case. With ensured robust stability, proposed applied case when LIP formulae single layer RBF neural network, simulation result validates superior performance each over original IIAL.
sci-hub.se 参考文章(35)
K. G. Vamvoudakis, F.L. Lewis, Shuzhi Sam Ge, Neural Networks in Feedback Control Systems Mechanical Engineers' Handbook. pp. 791- 825 ,(2015) , 10.1002/9781118985960.MEH223
Weisheng Chen, Shaoyong Hua, Shuzhi Sam Ge, Consensus-based distributed cooperative learning control for a group of discrete-time nonlinear multi-agent systems using neural networks Automatica. ,vol. 50, pp. 2254- 2268 ,(2014) , 10.1016/J.AUTOMATICA.2014.07.020
Gökhan M. Atınç, Dušan M. Stipanović, Petros G. Voulgaris, Supervised coverage control of multi-agent systems Automatica. ,vol. 50, pp. 2936- 2942 ,(2014) , 10.1016/J.AUTOMATICA.2014.10.023
Keng Peng Tee, Shuzhi Sam Ge, Eng Hock Tay, Barrier Lyapunov Functions for the control of output-constrained nonlinear systems Automatica. ,vol. 45, pp. 918- 927 ,(2009) , 10.1016/J.AUTOMATICA.2008.11.017
J. Park, I. W. Sandberg, Universal approximation using radial-basis-function networks Neural Computation. ,vol. 3, pp. 246- 257 ,(1991) , 10.1162/NECO.1991.3.2.246
S.S. Ge, G.Y. Li, T.H. Lee, Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems Automatica. ,vol. 39, pp. 807- 819 ,(2003) , 10.1016/S0005-1098(03)00032-3
Youfeng Su, Jie Huang, Cooperative Output Regulation of Linear Multi-Agent Systems IEEE Transactions on Automatic Control. ,vol. 57, pp. 1062- 1066 ,(2012) , 10.1109/TAC.2011.2169618
Cheng Song, Gang Feng, Yuan Fan, Yong Wang, Brief paper: Decentralized adaptive awareness coverage control for multi-agent networks Automatica. ,vol. 47, pp. 2749- 2756 ,(2011) , 10.1016/J.AUTOMATICA.2011.09.006
S. Jagannathan, F.L. Lewis, Discrete-time neural net controller for a class of nonlinear dynamical systems IEEE Transactions on Automatic Control. ,vol. 41, pp. 1693- 1699 ,(1996) , 10.1109/9.544013
Yan-Jun Liu, Shaocheng Tong, Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints Automatica. ,vol. 64, pp. 70- 75 ,(2016) , 10.1016/J.AUTOMATICA.2015.10.034