Steel and Composite Structures

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Smart modified repetitive-control design for nonlinear structure with tuned mass damper

  • ZY Chen (School of Science, Guangdong University of Petrochemical Technology) ;
  • Ruei-Yuan Wang (School of Science, Guangdong University of Petrochemical Technology) ;
  • Yahui Meng (School of Science, Guangdong University of Petrochemical Technology) ;
  • Timothy Chen (California Institute of Technology)
  • Received : 2022.01.08
  • Accepted : 2023.01.01
  • Published : 2023.01.10
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Abstract

A new intelligent adaptive control scheme was proposed that combines observer disturbance-based adaptive control and fuzzy adaptive control for a composite structure with a mass-adjustable damper. The most important advantage is that the control structures do not need to know the uncertainty limits and the interference effect is eliminated. Three adjustable parameters in LMI are used to control the gain of the 2D fuzzy control. Binary performance indices with weighted matrices are constructed to separately evaluate validation and training performance using the revalidation learning function. Determining the appropriate weight matrix balances control and learning efficiency and prevents large gains in control. It is proved that the stability of the control system can be ensured by a linear matrix theory of equality based on Lyapunov's theory. Simulation results show that the multilevel simulation approach combines accuracy with high computational efficiency. The M-TMD system, by slightly reducing critical joint load amplitudes, can significantly improve the overall response of an uncontrolled structure.

Keywords

Acknowledgement

The authors are grateful for the research grants given to Ruei Yuan Wang from the Projects of Talents Recruitment of GDUPT, Peoples R China under Grant NO. 2019rc098, and the research grants given to ZY Chen from the Projects of Talents Recruitment of GDUPT (NO. 2021rc002) in Guangdong Province, Peoples R China. as well as to the anonymous reviewers for constructive suggestions.

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