Abstract
This paper presents two novel harmonically disturbance-resistant zeroing neural network (ZNN) models: the known frequency harmonic-resistant ZNN (KFHRZNN) and the unknown frequency harmonic-resistant ZNN (UFHRZNN). These models are designed to tackle the pseudoinverse of time-varying matrices and inverse kinematics challenges in robotic manipulators. By precisely accounting for the derivatives of harmonic disturbances, they significantly mitigate these interferences, thereby improving the control efficacy of robots in high-speed, dynamic settings. The study elucidates the design rationale, convergence characteristics, and stability assessments for both KFHRZNN and UFHRZNN. Numerical simulations and physical experiments validate the effectiveness and advantages of these models in resolving time-varying issues within robotic manipulators, highlighting their precision and robustness against harmonic disturbances.
| Original language | English |
|---|---|
| Article number | 130930 |
| Journal | Neurocomputing |
| Volume | 651 |
| DOIs | |
| Publication status | Published - 28 Oct 2025 |
| MoE publication type | A1 Journal article-refereed |
Funding
This work was supported by the National Natural Science Foundation of China (No. 62271109) and the Sichuan Science and Technology Program of China (No. 2024NSFSC1492).
Keywords
- Dynamic matrix pseudoinverse
- Harmonic-resistant ZNN
- Manipulator inverse kinematics
- Zeroing neural network (ZNN)