Dynamic modeling and neural network compensation for rotating Euler-Bernoulli beam using a novel deformation description method

Abstract

Dynamic models that consider multiple nonlinear factors including the two-dimensional deformation and higher-order modes are too computationally complex to be suitable for real-time control of the rotating Euler-Bernoulli beam. This article proposes a new dynamic modeling method according to the assumed mode method. The new modeling method not only has high modeling accuracy but also has a brief mathematical expression. In addition, based on the new modeling method, a neural network sliding mode control strategy using the saturation function is designed to improve the rotational accuracy of the Euler-Bernoulli beam. In the control law design, the saturation function is proposed instead of the unit step function to reduce the chattering phenomenon. Simulation and control experiments describe that the new dynamic modeling method has higher modeling accuracy than the modeling method based on the one-dimensional deformation; the proposed strategy can effectively decrease the rotation angle tracking error of the rotating Euler-Bernoulli beam and improve the control accuracy.

Keywords:

• Euler-Bernoulli beam
• dynamic modeling
• neural network compensation
• deformation descriptor method

Authors’ contribution

Dongyang Shang: Methodology, Writing. Xiaopeng Li: Supervision, Formal analysis. Men Yin: Software, Visualization. Fanjie Li: Validation, Visualization.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Additional information

Funding

The project is supported by the Applied Basic Research Program of Liaoning Province (2023JH2/101300159), the National Natural Science Foundation of China (No. 52275090), the Fundamental Research Funds for the Central Universities (N2103025), and the National Key Research and Development Program of China (2020YFB2007802).