Deformation control of piezoelectric intelligent curved shell structure with multiple actuators based on neural network optimized by genetic algorithm

• A back propagation (BP) neural network method based on genetic algorithm optimization is proposed for predicting the input voltage of each macro fiber composite (MFC) for the piezoelectric intelligent curved shell structure with multiple actuators . • The predicted voltages of MFC are used in the collaborative simulation technology to complete the deformation control simulation of piezoelectric curved shell. • The effectiveness of the proposed method is verified through experiments on the piezoelectric intelligent curved shell structure. In aerospace engineering, it is important to achieve precise deformation control of curved shell. In this paper, the piezoelectric intelligent curved shell structure (PICSS) with multiple actuators was selected as the research object. Firstly, a finite element model of the structure was constructed, and the sample data was obtained based on simulation method for the back propagation (BP) neural network. Next, the structure of the BP neural network was determined as ‘6–13-24-3′ with the goal of obtaining the minimum mean square error. Then genetic algorithm was used to optimize the initial weights and thresholds of the BP neural network, and the input voltage of each actuator was predicted. The accuracy of voltage prediction was confirmed through collaborative simulation technology by testing the BP neural network before and after optimization. Finally, the deformation perception and control of the piezoelectric curved shell were verified through the PICSS.