Active Disturbance Rejection Control Based Trajectory Tracking Method for Robotic Multi-Target Riveting Tasks
To improve riveting efficiency, a multi-rivet pressing end effector and an active disturbance rejection control (ADRC) controller with model uncertainties were designed to achieve precise trajectory tracking of the end effector. First, a multi-rivet pressing end effector was developed, enabling simultaneous riveting of multiple rivets. A motor-driven cam mechanism regulates the retraction/extension of pressing rods. In contrast, the distance between two pressing rods is adjusted via movable linkages to prevent interference with non-operational riveting components. Finite element analysis (FEA) was employed to verify the structural feasibility and reliability of the mechanism. Second, an improved ADRC controller was designed to address uncertainties and nonlinearities in the riveting robot’s dynamic model, significantly enhancing control performance under complex working conditions. High-precision trajectory tracking control of the end effector was achieved by establishing the robot’s dynamic model and ADRC controller. The convergence of trajectory tracking errors under incomplete inertia matrices was analyzed, and system stability was rigorously proven using Lyapunov functions. Simulation results demonstrate that the designed end effector meets practical strength requirements, with a maximum displacement deviation of 0.0317 mm, well within acceptable tolerances. The improved ADRC algorithm exhibits rapid and accurate trajectory tracking capabilities under model uncertainties, nonlinearities, and time-varying conditions, confirming its superior robustness.
Authors
- Jingmin Xin (ORCID: https://orcid.org/0000-0003-1906-2327)
- Yang Du (ORCID: https://orcid.org/0000-0003-2254-778X)
- Zonggang Li (ORCID: https://orcid.org/0000-0002-4908-8299)
- Yujian Yin (ORCID: https://orcid.org/0009-0002-4316-1528)
- J. B. Jiao (ORCID: https://orcid.org/0000-0002-9947-9191)
Institutions
- Lanzhou Jiaotong University (CN)
Publication Details
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1109/icaisisas64483.2025.11052205
- Primary Topic
- Advanced Manufacturing and Logistics Optimization
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China