A Robust Predefined-Time Convergence Zeroing Neural Network for Dynamic Matrix Inversion

As a classical and effective method for solving various time-varying problems, the zeroing neural network (ZNN) is widely applied in the scientific and industrial realms. In plentiful studies on the ZNN model, its robustness and convergence have been two essential criteria to evaluate the quality of...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2023-06, Vol.53 (6), p.3887-3900
Main Authors: Jin, Jie, Zhu, Jingcan, Zhao, Lv, Chen, Lei, Chen, Long, Gong, Jianqiang
Format: Article
Language:English
Subjects:
Analytical models
Computational modeling
Convergence
Dynamic matrix inversion
Mathematical models
Neural networks
Optimization
predefined-time convergence
Real-time systems
Robot arms
Robot control
robotic manipulator (RM)
robust predefined-time convergence zeroing neural network (RPTCZNN)
Robustness
Robustness (mathematics)
Tracking control
Upper bounds
well-designed activation function (WDAF)
zeroing neural network (ZNN)
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Summary:As a classical and effective method for solving various time-varying problems, the zeroing neural network (ZNN) is widely applied in the scientific and industrial realms. In plentiful studies on the ZNN model, its robustness and convergence have been two essential criteria to evaluate the quality of the model. Improvements in the ZNN model have been focused on its convergence speed; however, the adjustability of its convergence speed has been neglected in most prior works, which restricts its extensive promotion in practical application. Considering the above-mentioned issue, a well-designed activation function (WDAF) is designed. Based on the WDAF, a robust predefined-time convergence ZNN (RPTCZNN) model with adjustable convergence speed is proposed to solve the dynamic matrix inversion problem. In addition, the upper bound of the RPTCZNN model's convergence time is theoretically validated by strict mathematical analysis in a noiseless and noisy environment. Finally, several simulation experiments of the proposed model are conducted to find solutions of dynamic matrix inversion with different dimensions. Moreover, the realization of the tracking control of the robotic manipulator further illustrates the model's superior convergence and robustness.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2022.3179312