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A time controlling neural network for time‐varying QP solving with application to kinematics of mobile manipulators
Author(s) -
Kong Ying,
Jiang Yunliang,
Zhou Junwen,
Wu Huifeng
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22304
Subject(s) - control theory (sociology) , artificial neural network , kinematics , computer science , robustness (evolution) , convergence (economics) , synchronism , nonlinear system , trajectory , mathematics , artificial intelligence , control (management) , physics , classical mechanics , computer network , biochemistry , chemistry , asynchronous communication , quantum mechanics , astronomy , economics , gene , economic growth
To obtain the solution for time‐varying quadratic programming (QP), a time controlling neural network (TCNN) is presented and discussed. The traditional recurrent neural networks provide a prospect for real‐time calculations and repeatable trajectory control of the mobile manipulators due to its high executing processing and nonlinear disposal ability. However, the convergent time is still a considerable point for the solution of a dynamic system dealing with synchronism and robustness. In this note, a TCNN model by incorporating an initial rectified term is applied to solve the online calculation problems and the convergent time can be controlled in advance. Theoretical analyses on stability, prespecified time and convergence are rigorously clarified. Finally, effectiveness and precision of the TCNN model for the solution of a QP example have been verified. In addition, a repetitive trajectory planning for a three‐wheel manipulator is introduced to demonstrate the superiority of the TCNN.