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An offset error compensation method for improving ANN accuracy when used for position control of precision machinery

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Abstract

Artificial Neural Networks (ANNs) have recently become the focus of considerable attention in many disciplines, including robot control, where they can be used as a general class of nonlinear models to solve highly nonlinear control problems. Feedforward neural networks have been widely applied for modelling and control purposes. One of the ANN applications in robot control is for the solution of the inverse kinematic problem, which is important in path planning of robot manipulators. This paper proposes an iterative approach and an offset error compensation method to improve the accuracy of the inverse kinematic solutions by using an ANN and a forward kinematic model of a robot. The offset error compensation method offers potential to generate accurately the inverse solution for a class of problems which have an easily obtained forward model and a complicated solution.

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Authors and Affiliations

  1. Manufacturing Division, Department of Mechanical Engineering, UMIST, PO Box 88, M60 1QD, Manchester, UK

    Y. F. Lou &  P. Brunn

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  1. Y. F. Lou
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  2. P. Brunn
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Correspondence to P. Brunn.

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Now Lecturing in Taiwan.

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Lou, Y.F., Brunn, P. An offset error compensation method for improving ANN accuracy when used for position control of precision machinery. Neural Comput & Applic 7, 90–95 (1998). https://doi.org/10.1007/BF01413713

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