Model-free Adaptive De-noising Control and Its Application

被引:0
|
作者
Dong N. [1 ]
Zhu S. [1 ]
机构
[1] School of Electrical and Information Engineering, Tianjin University, Tianjin
来源
Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences | 2020年 / 47卷 / 08期
基金
中国国家自然科学基金;
关键词
Circulating fluidized bed(CFB) boiler; Control algorithm; Control of industrial boilers; De-noise; Model-free adaptive control; Tracking differentiator;
D O I
10.16339/j.cnki.hdxbzkb.2020.08.009
中图分类号
学科分类号
摘要
In order to make the model-free adaptive control(MFAC) better solve the control problem with noisy system, this paper examines the noise system and applies improved tracking differentiator in MFAC. Filtering is performed in the feedback process, and a MFAC method based on improved tracking differentiator(ITD) is proposed. The convergence of the algorithm is also proved. The simulation experiment proves that the improved control method can quickly track a given signal and has good resistance to noise interference. Finally, it is applied to the control of the drum water level of circulating fluidized bed (CFB) boiler. © 2020, Editorial Department of Journal of Hunan University. All right reserved.
引用
收藏
页码:74 / 81
页数:7
相关论文
共 20 条
  • [1] SHA Y, FAN Q W, ZHANG L L, Et al., FAST node displacement control method using self-tunning fuzzy PID control, Journal of Northeastern University (Natural Science), 39, 4, pp. 487-491, (2018)
  • [2] QIU Z Z, LI S F., Modeling and simulation of PID networked control systems based on neural network, Journal of System Simulation, 30, 4, pp. 1423-1432, (2018)
  • [3] ZHANG K, ZHANG X Q, ZHANG L M, Et al., A novel approach for optimization of polymer-surfactant flooding based on simultaneous perturbation stochastic approximation algorithm, Journal of China University of Petroleum (Edition of Natural Science), 41, 5, pp. 102-109, (2017)
  • [4] ZHOU Y, HE L, ZHENG F., Iterative learning control for linear motor system with time delay and data dropout, Control Theory and Applications, 34, 12, pp. 1631-1636, (2017)
  • [5] CAMPI M C, LECCHINI A, SAVARESI S M., Virtual reference feedback tuning: a direct method for the design of feedback controllers, Automatical, 38, 8, pp. 1337-1346, (2002)
  • [6] HOU Z S., Nonlinear system parameter identification, adaptive control and model-free adaptive control, pp. 26-92, (1994)
  • [7] HOU M D, WANG Y S., A model-free adaptive integral terminal sliding mode control method, Control and Decision, 33, 9, pp. 1591-1597, (2018)
  • [8] ZHU Y M, HOU Z S., Data-driven MFAC for a class of discrete-time nonlinear with RBFNN, IEEE Transactions on Neural Networks and Learning Systems, 25, 5, pp. 1013-1020, (2014)
  • [9] HAN Z G, XU M X., The general form of model-free control law and its application in petrochemical industry, Journal of Natural Science of Heilongjiang University, 18, 3, pp. 24-34, (2001)
  • [10] TIAN J L, QU W D., Analysis of modified method for threshold functions based on wavelet de-noising, Chemical Automation and Instrumentation, 44, 3, pp. 243-247, (2017)