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Zeroing neural network for bound-constrained time-varying nonlinear equation solving and its application to mobile robot manipulators

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Abstract

A typical class of recurrent neural networks called zeroing neural network (ZNN) has been considered as a powerful alternative for time-varying problems solving. In this paper, a new ZNN model is proposed and studied to solve the bound-constrained time-varying nonlinear equation (BCTVNE). Specifically, by introducing a time-varying nonnegative vector, the BCTVNE is reformulated as a combined system of nonlinear equations. On the basis of two indefinite error functions and the exponential decay formula, the new ZNN model is thus developed, which can zero in on the combined system. Theoretical analysis and simulation results are provided to verify the effectiveness of the proposed ZNN model. The applicability is further indicated under the simulations on an omnidirectional mobile robot manipulator via the proposed ZNN model.

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Acknowledgements

The authors would like to thank the editors and reviewers for the time and effort they spent reviewing this paper as well as for their detailed and constructive comments for the paper improvement in terms of presentation and quality.

Funding

This paper is supported by the Basic Scientific Research Project for University of Heilongjiang Province with Number being 135409611, and also the Quanzhou City Science and Technology Program of China with Number being 2018C111R.

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Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China

    Zhisheng Ma

  2. College of Science, Qiqihar University, Qiqihar, 161006, China

    Shihang Yu & Yang Han

  3. College of Information Science and Engineering, Huaqiao University, Xiamen, 361021, China

    Dongsheng Guo

Authors
  1. Zhisheng Ma
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  2. Shihang Yu
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  3. Yang Han
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  4. Dongsheng Guo
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Correspondence to Zhisheng Ma.

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Z. Ma declares that he has no conflict of interest. S. Yu declares that he has no conflict of interest. Y. Han declares that he has no conflict of interest. D. Guo declares that he has no conflict of interest.

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Ma, Z., Yu, S., Han, Y. et al. Zeroing neural network for bound-constrained time-varying nonlinear equation solving and its application to mobile robot manipulators. Neural Comput & Applic 33, 14231–14245 (2021). https://doi.org/10.1007/s00521-021-06068-6

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  • DOI: https://doi.org/10.1007/s00521-021-06068-6

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