Noise-Tolerant Terminal Zeroing Neural Networks for Solving Time-Varying Quadratic Programming: A Triple Power-Rate Speeding-Up Strategy

ZHONG Guomin; XIAO Likun; WANG Liming; SUN Mingxuan

doi:10.11999/JEIT250128

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ZHONG Guomin, XIAO Likun, WANG Liming, SUN Mingxuan. Noise-Tolerant Terminal Zeroing Neural Networks for Solving Time-Varying Quadratic Programming: A Triple Power-Rate Speeding-Up Strategy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250128

Citation:

ZHONG Guomin, XIAO Likun, WANG Liming, SUN Mingxuan. Noise-Tolerant Terminal Zeroing Neural Networks for Solving Time-Varying Quadratic Programming: A Triple Power-Rate Speeding-Up Strategy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250128

Citation:

ZHONG Guomin, XIAO Likun, WANG Liming, SUN Mingxuan. Noise-Tolerant Terminal Zeroing Neural Networks for Solving Time-Varying Quadratic Programming: A Triple Power-Rate Speeding-Up Strategy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250128

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Noise-Tolerant Terminal Zeroing Neural Networks for Solving Time-Varying Quadratic Programming: A Triple Power-Rate Speeding-Up Strategy

doi: 10.11999/JEIT250128 cstr: 32379.14.JEIT250128

1.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
2.
Academy for Advanced Interdisciplinary Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China

Funds: The National Natural Science Foundation of China (62073291, 62222315)

Received Date: 2025-03-05
Rev Recd Date: 2025-08-28

Available Online: 2025-09-02

Abstract

Abstract

Objective The computational performance of Zeroing Neural Networks (ZNNs) is enhanced by introducing additional power terms into the activation function. However, this strategy complicates the derivation of explicit settling time expressions. To address this issue, a triple power-rate activation function is designed, and a power-rate speeding-up noise-tolerant terminal ZNN is constructed, through which an exact expression for the settling time is derived. In previous studies, the optimization criterion parameter for repetitive motion planning was typically constant, which may reduce the operational efficiency of robotic manipulators. To overcome this limitation, a time-varying parameter optimization criterion is developed to satisfy task requirements at different stages of repetitive motion planning, thereby improving the operational efficiency of redundant robotic manipulators during task execution. Methods A triple power-rate activation function is proposed, extending the conventional bi-power activation function, and a power-rate speeding-up noise-tolerant terminal ZNN is constructed. The convergence process under different parameter settings is analyzed, and explicit settling time expressions are derived. Theoretical analysis confirms that the proposed neural network can effectively suppress vanishing noise. For the repetitive motion planning problem of redundant manipulators, the power-rate speeding-up noise-tolerant terminal ZNN is employed as a solver to ensure acquisition of the desired end-effector trajectory within fixed time. To address the limitations of constant-parameter optimization criteria in repetitive motion planning, a time-varying parameter optimization criterion is designed, which demonstrably improves the operational efficiency of redundant manipulators. Results and Discussions In this study, the power-rate speeding-up noise-tolerant terminal ZNN is employed together with bi-power-rate terminal ZNNs to solve time-varying quadratic programming problems. Simulation results show that the proposed power-rate speeding-up noise-tolerant terminal ZNN achieves a faster convergence rate (Fig.2(a), Fig.2(b)) and demonstrates improved capability in suppressing vanishing noise (Fig.2(c)). The convergence process of neural computational error under different parameter conditions is analyzed without noise (Fig 3). Furthermore, the power-rate speeding-up noise-tolerant terminal ZNN is applied to the repetitive motion planning problem of redundant manipulators. Its effectiveness in solving repetitive motion planning is validated (Fig. 4), and the integration of a time-varying parameter optimization criterion further enhances the operational efficiency of redundant manipulators (Fig. 5). Conclusions A power-rate speeding-up noise-tolerant terminal ZNN is proposed for solving time-varying quadratic programming problem with time-varying equality constraints, ensuring fixed time convergence of neural computing errors. Compared with conventional bi-power-rate terminal ZNNs, the proposed network achieves faster convergence and stronger noise-tolerance performance. To address the limitations of constant-parameter optimization criteria in repetitive motion planning, a time-varying parameter optimization criterion is designed and shown to improve the operational efficiency of redundant manipulators.
- Zeroing Neural Networks (ZNNs),
- Triple power-rate activation function,
- Quadratic programming,
- Repetitive motion planning.

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References(30)

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