In context
Parallel robots with two rotational and one translational (2R1T) degrees of freedom are increasingly used as positioning platforms or machining spindles in five-axis machine tools, where high-precision repetitive trajectory tracking is essential for batch processing of complex curved surfaces. However, their coupled branches and passive joints introduce nonlinearities that challenge conventional model-based control methods.
What was reported
Researchers at Zhejiang Sci-Tech University proposed an adaptive sliding-mode iterative learning control (ASMILC) scheme for a 2UPR-RPU parallel robot. The approach integrates iterative learning control to improve tracking accuracy over repeated trajectories, sliding-mode control to enhance robustness against disturbances, and adaptive control to mitigate chattering caused by sliding-mode switching terms.
The controller's convergence was proven using the Bellman-Gronwall theorem and λ-norm analysis, while stability was verified via a Lyapunov-like function. Both numerical simulations and prototype experiments were conducted on the 2R1T parallel robot.
Experimental results showed that the proposed ASMILC reduced the mean error, maximum error, and standard deviation in joint space by 59.5%, 55.1%, and 60.1%, respectively, compared with a PD control baseline.
Why it mattered
This work addresses a gap in iterative learning control research, which has focused mostly on serial robots. By combining multiple control strategies, it offers a practical solution for high-precision repetitive machining tasks in parallel robots, potentially improving the performance of five-axis machine tools and other industrial automation systems that rely on parallel kinematics.
Under this control algorithm, the mean error, maximum error and standard deviation of the joint space of the parallel robot are reduced by 59.5%, 55.1% and 60.1% compared with the PD control method.
Source: 《机器人》期刊 (robot.sia.cn) · Published 2024-05-08 · “2R1T并联机器人滑模自适应迭代学习控制”