Integrated design optimization of actuator layout and structural ply parameters for the dynamic shape control of piezoelectric laminated curved shell structures

Zheng, Hao; Zhang, Shunqi; Zhao, Guozhong

doi:10.1007/s00158-020-02818-7

Cited by 8 publications

(3 citation statements)

References 37 publications

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“…Where B denotes the strain matrix; φ B denotes the electrical strain matrix; and e is the piezoelectric stress coefficient matrix. The specific expressions of all the above matrices can be referred to in the literature [8].…”

Section: Dynamic Finite Element Equationsmentioning

confidence: 99%

“…In the Newmark integral method, the velocity vector u &, acceleration vector u & &, and displacement vector u at discrete time points i t and 1 i t  meet the following relationships [8]:…”

Section: Obtaining the Optimal Voltages With Time-varying Characteristicmentioning

confidence: 99%

“…Where t  represents the time interval;  and  are constants that can be found in [8]. By analyzing each time step, i u is found to be:…”

Section: Obtaining the Optimal Voltages With Time-varying Characteristicmentioning

confidence: 99%

See 2 more Smart Citations

Active control and prediction model for the dynamic shape of laminated plate/shell structures with piezoelectric actuators

Zheng,

Wang,

Zhou

et al. 2024

J. Phys.: Conf. Ser.

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This study establishes an active control and prediction model for the dynamic shape of laminated plates/shells with piezoelectric actuators. Firstly, transient responses of shell structures are numerically calculated by adopting the finite element method (FEM) and the Newmark formulae, then the Kuhn-Tucker equation is utilized to get the time-varying voltage values with a minimum shape control error. Subsequently, a backpropagation neural network (BPNN) is constructed to predict the control precision for cases with different piezoelectric actuator layouts. Finally, the proposed control model is demonstrated through numerical examples with a maximum relative control error of only 0.023% for a given desired shape and the average error of the prediction model with 98 input neurons is 18%, thereby satisfying the application requirements. Furthermore, for a model with 3870 degrees of freedom and 21 discrete time points, the computational time is reduced significantly from 8.24 s to 0.002 s through the prediction model, making it highly suitable for optimization problems by utilizing heuristic algorithms.

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Section: Dynamic Finite Element Equationsmentioning

confidence: 99%

Section: Obtaining the Optimal Voltages With Time-varying Characteristicmentioning

confidence: 99%

See 1 more Smart Citation

Active control and prediction model for the dynamic shape of laminated plate/shell structures with piezoelectric actuators

Zheng,

Wang,

Zhou

et al. 2024

J. Phys.: Conf. Ser.

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Design optimization and sensitivity analysis on time-domain sound radiation of laminated curved shell structures

Zheng

Zhao

et al. 2022

Struct Multidisc Optim

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Robust monolithic versus distributed control/structure co-optimization of flexible space systems in presence of parametric uncertainties

Sanfedino,

Alazard,

Kiley

et al. 2023

Struct Multidisc Optim

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Integrated design optimization of actuator layout and structural ply parameters for the dynamic shape control of piezoelectric laminated curved shell structures

Cited by 8 publications

References 37 publications

Active control and prediction model for the dynamic shape of laminated plate/shell structures with piezoelectric actuators

Active control and prediction model for the dynamic shape of laminated plate/shell structures with piezoelectric actuators

Design optimization and sensitivity analysis on time-domain sound radiation of laminated curved shell structures

Robust monolithic versus distributed control/structure co-optimization of flexible space systems in presence of parametric uncertainties

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