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Supervised learning based on the self-organizing maps for forward kinematic modeling of Stewart platform

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Abstract

In this study, we propose an alternative technique for solving the forward kinematic problem of parallel manipulator which is designed based on generalized Stewart platform. The focus of this work is to predict a pose vector of a moving plate from a given set of six leg lengths. Since the data of parallel kinematics are usually available in the form of nonlinear dynamic system, several methods of system identification have been proposed in order to construct the forward kinematic model and approximate the pose vectors. Although these methods based on a multilayer perceptron (MLP) neural network provide acceptable results, MLP training suffers from convergence to local optima. Thus, we propose to use an alternative supervised learning algorithm called vector-quantized temporal associative memory (VQTAM) instead of MLP-based methods. VQTAM relying on self-organizing map architecture is used to build the mapping from the input space to the output space such that the training/testing datasets are generated from inverse kinematic model. The solutions from standard VQTAM are improved by an autoregressive (AR) model and locally linear embedding (LLE). The experimental results indicate that VQTAM with AR/LLE gives the outputs with nearly 100% prediction accuracy in the case of smooth data, while VQTAM + LLE provides the most accurate prediction on noisy data. Therefore, VQTAM + LLE is a very robust estimation method and can practically be used for solving the forward kinematic problem.

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Acknowledgements

The first author was supported by Development and Promotion of Science and Technologies Talents, Thailand. The second author was supported by the National Research Council of Thailand and Khon Kaen University, Thailand (Grant No. 580046). This research is partially supported by the Centre of Excellence in Mathematics, the Commission on Higher Education, Thailand.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand

    Saithip Limtrakul & Banchar Arnonkijpanich

  2. Centre of Excellence in Mathematics, CHE, Bangkok, 10400, Thailand

    Banchar Arnonkijpanich

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  1. Saithip Limtrakul
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  2. Banchar Arnonkijpanich
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Correspondence to Banchar Arnonkijpanich.

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Limtrakul, S., Arnonkijpanich, B. Supervised learning based on the self-organizing maps for forward kinematic modeling of Stewart platform. Neural Comput & Applic 31, 619–635 (2019). https://doi.org/10.1007/s00521-017-3095-4

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