Compensation of Process Forces with a Model-Based Feed-Forward Control for Robot Machining

Gründel, L.; Lienenlüke, Lars; Storms, Simon; Brecher, Christian

doi:10.1109/iccre.2019.8724312

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…the development of the adaptive model and partial modelfree control algorithms. For example, Grundel and his colleagues designed a model-based controller considering the external cutting forces, which enables the system to cancel the effect of the process forces while operation [11]; Fan et al proposed a fuzzy adaptation algorithm to compensate the uncertain terms in the dynamics [12].…”

Section: Introductionmentioning

confidence: 99%

Learning Feedforward Control for Industrial Manipulators

Liu

Popov

Turner

et al. 2021

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)

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In this work, an iterative learning control (ILC) algorithm is proposed for industrial manipulators. The proposed ILC algorithm works coordinately with the inverse dynamics of the manipulator and a feedback controller. The entire control scheme has the ability of compensating both repetitive and non-repetitive disturbances; guaranteeing the control accuracy of the first implementation; and improving the control accuracy of the manipulator progressively with successive iterations. In order to build the the convergence of the proposed ILC algorithm, a composite energy function is developed. A case study on a four degree of freedom industrial manipulator is demonstrated to illustrate the effectiveness of the proposed control scheme. By implementing the ILC algorithm, the maximum root mean square error of the control accuracy is improved from 0.0262 rad to 0.0016 rad within ten iterations.

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Section: Introductionmentioning

confidence: 99%