MIMO ILC using complex-kernel regression and application to Precision SEA robots

MIMO ILC using complex-kernel regression and application to Precision SEA robots
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使用复杂内核回归的 MIMO ILC 及其在 Precision SEA 机器人中的应用

DOI:
10.1016/j.automatica.2021.109550
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发表时间:
2021
期刊:
影响因子:
6.4
通讯作者:
Devasia, Santosh
Devasia, Santosh
中科院分区:
计算机科学2区
文献类型:
--
作者:
Yan, Leon;Banka, Nathan;Owan, Parker;Piaskowy, Walter Tony;Garbini, Joseph L.;Devasia, Santosh

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This work improves the positioning precision of lightweight robots with series elastic actuators (SEAs). Lightweight SEA robots, along with low-impedance control, can maneuver without causing damage in uncertain, confined spaces such as inside an aircraft wing during aircraft assembly. Nevertheless, substantial modeling uncertainties in SEA robots reduce the precision achieved by model-based approaches such as inversion-based feedforward. Therefore, this article improves the precision of SEA robots around specified operating points, through a multi-input multi-output (MIMO), iterative learning control (ILC) approach. The main contributions of this article are to (i) introduce an input-weighted complex kernel to estimate local MIMO models using complex Gaussian process regression (c-GPR); (ii) develop Geršgorin-theorem-based conditions on the iteration gains for ensuring ILC convergence to precision within noise-related limits, even with errors in the estimated model; and (iii) demonstrate precision positioning with an experimental SEA robot. Comparative experimental results, with and without ILC, show around 90% improvement in the positioning precision (close to the repeatability limit of the robot) and a 10-times increase in the SEA robot’s operating speed with the use of the MIMO ILC.
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