Study on iterative learning control of shaker based on NARX neural network system identification
Aiming at the poor control effect of the traditional shaker,an adaptive iterative learning control algorithm was proposed,which constructs an external displacement closed-loop on the basis of the original displacement three-parameter control system to form a double closed-loop control system.At the same time,in order to more accurately simulate the dynamic behavior of the shaker,the Gray Wolf Optimization(GWO)algorithm was introduced to optimize the nonlinear organic originated regression neu-ral network(Nonlinear Auto-Regressive with exogeneous inputs neural network,NARX)for shaker model identification.The sim-ulation results show that the shaker model identification using GWO-NARX neural network achieves a high identification effect with an accuracy of 99.8％.On the basis of identification model,the control accuracy of the shaker was greatly improved by using the adaptive iterative learning control algorithm,and the maximum error is reduced by 49.6％compared with the original system.Compared with the traditional NARX neural network for shaker model identification,the GWO-NARX neural network has a better identification effect and the model is closer to the real system;compared with the traditional three-parameter control sys-tem,the adaptive iterative learning control algorithm improves the accuracy of shaker waveform reproduction and can better adapt to the complexity of the system.It provides reliable technical support and solutions for practical engineering applications.
国家科技期刊平台
NSTL
万方数据
