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Robust Control of a Two-Link Flexible Manipulator with Quasi-Static Deflection Compensation Using Neural Networks

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Abstract

A robust control method of a two-link flexible manipulator with neural networks based quasi-static distortion compensation is proposed and experimentally investigated. The dynamics equation of the flexible manipulator is divided into a slow subsystem and a fast subsystem based on the assumed mode method and singular perturbation theory. A decomposition based robust controller is proposed with respect to the slow subsystem, and H ∞ control is applied to the fast subsystem. The overall closed-loop control is determined by the composite algorithm that combines the two control laws. Furthermore, a neural network compensation scheme is also integrated into the control system to compensate for quasi-static deflection. The proposed control method has been implemented on a two-link flexible manipulator for precise end-tip tracking control. Experimental results are presented in this paper along with concluding remarks.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Jilin University, Changchun, China, 130025

    Y. Li, T. Hong & K. Liu

  2. Department of Aerospace Engineering, Ryerson University, Toronto, Ontario, Canada, MSB 2K3

    G. Liu

Authors
  1. Y. Li
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  2. G. Liu
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  3. T. Hong
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  4. K. Liu
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Correspondence to G. Liu.

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Li, Y., Liu, G., Hong, T. et al. Robust Control of a Two-Link Flexible Manipulator with Quasi-Static Deflection Compensation Using Neural Networks. J Intell Robot Syst 44, 263–276 (2005). https://doi.org/10.1007/s10846-005-9019-z

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  • DOI: https://doi.org/10.1007/s10846-005-9019-z

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