PID control based on GCAQBP algorithm

作     者：Wang, Qizhi Wang, Xiaoxia Sun, Bingyang 

作者机构：College of Mechanical Engineering and Automation Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment Fujian Province University Huaqiao University Xiamen361021 China College of Computer Science and Technolog Huaqiao University Xiamen361021 China College of Mechanical Engineering and Automation Huaqiao University Xiamen361021 China 

出 版 物：《IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association》 (IPPTA)

年 卷 期：2018年第30卷第3期

页      面：161-165页

核心收录：

学科分类：0711[理学-系统科学] 082903[工学-林产化学加工工程] 08[工学] 0829[工学-林业工程] 082201[工学-制浆造纸工程] 0811[工学-控制科学与工程] 0822[工学-轻工技术与工程] 

基　　金：This work was support by Support Huaqiao university horizontal project: Z1543085 43201142 

主　　题：Neural networks 

摘      要：The conventional PID controller cannot have a satisfactory performance when it has been applied in the nonlinear system. In this paper, GCAQBP algorithm for complex-valued neural networks is proposed in control system. The approach based on an artificial neural network (ANN) is presented for control system. The ANN model is based on GCAQBP (Globally Convergent Adaptive Quick Back Propagation) algorithm. The method adopts the advanced algorithm of BPNN for nonlinear system to estimate the parameters of PID, increase the control signal and reduce the error results. Simulations show it has high robustness, simulation results are also presented to exhibit the effectiveness of the proposed GCAQBP algorithm. © 2018 Indian Pulp and Paper Technical Association. All rights reserved.