Multi-parameter identification of earthquake simulation shaking table based on BP neural network - Gao, Chunhua; Li, Cun; Qin, Mengyuan; Yang, Yanping; Yuan, Zihan (Frontiers in Physics, 2024)
In this paper, a multi-parametric (BP-MVC) controller based on BP neural network is proposed and applied to the control of shaker system. The controller has the advantages of easy parameterization and robustness. According to the requirements of the system performance, under the theoretical parameter control, the multi-variable (MVC) controller is better than the three-variable (TVC) controller, but the control effect is not good, and the parameter tuning is needed to achieve a good control effect. The BP neural network is applied to the multi-parameter (MVC) controller to recognize the shaker model, adjust the parameters in real time, accelerate the convergence speed and reduce the system error. The BP-MVC controller is simulated and verified, and the correlation (CC) is improved to more than 0.985, and the root mean square error (RMSE) and mean absolute error (MAE) are reduced to less than 0.04 and 0.25, respectively, in the nonlinear and time-varying hydraulic system, which proves that the BP-MVC controller has better control performance and adaptivity.