2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

输入饱和的一类切换系统神经网络跟踪控制

司文杰 董训德 王聪

司文杰, 董训德, 王聪. 输入饱和的一类切换系统神经网络跟踪控制. 自动化学报, 2017, 43(8): 1383-1392. doi: 10.16383/j.aas.2017.c160372
引用本文: 司文杰, 董训德, 王聪. 输入饱和的一类切换系统神经网络跟踪控制. 自动化学报, 2017, 43(8): 1383-1392. doi: 10.16383/j.aas.2017.c160372
SI Wen-Jie, DONG Xun-De, WANG Cong. Adaptive Neural Tracking Control Design for a Class of Uncertain Switched Nonlinear Systems with Input Saturation. ACTA AUTOMATICA SINICA, 2017, 43(8): 1383-1392. doi: 10.16383/j.aas.2017.c160372
Citation: SI Wen-Jie, DONG Xun-De, WANG Cong. Adaptive Neural Tracking Control Design for a Class of Uncertain Switched Nonlinear Systems with Input Saturation. ACTA AUTOMATICA SINICA, 2017, 43(8): 1383-1392. doi: 10.16383/j.aas.2017.c160372

输入饱和的一类切换系统神经网络跟踪控制

doi: 10.16383/j.aas.2017.c160372
基金项目: 

国家重大科研仪器研制项目 61527811

详细信息
    作者简介:

    董训德  华南理工大学自动化科学与工程学院助理研究员.主要研究方向为系统识别, 动态模式辨识和确定学习理论研究.E-mail:audxd@scut.edu.cn

    王聪  华南理工大学自动化学院教授.主要研究方向为非线性系统自适应神经网络控制与辨识, 确定学习理论, 动态模式识别, 基于模式的智能控制, 振动故障诊断及在航空航天, 生物医学工程等领域的应用.E-mail:wangcong@scut.edu.cn

    通讯作者:

    司文杰 华南理工大学自动化科学与工程学院博士后.主要研究方向为自适应控制, 确定学习和故障诊断.本文通信作者.E-mail:mesiwenjie@scut.edu.cn

Adaptive Neural Tracking Control Design for a Class of Uncertain Switched Nonlinear Systems with Input Saturation

Funds: 

National Research and Development Program for Major Research Instruments 61527811

More Information
    Author Bio:

    Assistant professor at the School of Automation Science and Engineering, South China University of Technology. His research interest covers system identification, dynamical pattern recognition and deterministic learning theory

    Professor at the School of Automation, South China University of Technology. His research interest covers adaptive neural network control and identification of nonlinear systems, deterministic learning theory, dynamical pattern recognition, pattern-based intelligent control, oscillation fault diagnosis, and applications in aerospace and biomedical engineering

    Corresponding author: SI Wen-Jie Post-doctor at the School of Automation Science and Engineering, South China University of Technology. His research interest covers adaptive control, deterministic learning and fault diagnosis. Corresponding author of this paper
  • 摘要: 针对单输入单输出系统研究一种在任意切换下的跟踪控制问题,系统包含未知扰动和输入饱和特性.首先,利用高斯误差函数描述一个连续可导的非对称饱和模型.其次,利用径向基神经网络(Radial basis function neural network,RBF NN)逼近未知的系统动态.最后,基于公共的Lyapunov函数构造状态反馈控制器.设计的控制器避免过多参数调节从而减轻计算负荷.结果展示本文给出的状态反馈控制器可以保证闭环系统的所有信号是半全局一致有界的,并且跟踪误差可收敛到零值小的领域内.最后的仿真结果进一步验证提出方法的有效性.
    1)  本文责任编委 孙希明
  • 图  1  饱和函数

    Fig.  1  Saturation functions

    图  2  跟踪性能

    Fig.  2  Tracking performances

    图  3  跟踪误差 ${ y-y_d}$

    Fig.  3  The tracking error ${ y-y_d}$

    图  4  控制输入

    Fig.  4  Control inputs

    图  5  自适应更新率 $\hat{\theta}_1$

    Fig.  5  Response of the adaptive law $\hat{\theta}_1$

    图  6  自适应更新率 $\hat{\theta}_2$

    Fig.  6  Response of the adaptive law $\hat{\theta}_2$

    图  7  切换信号

    Fig.  7  Switching signal

    图  8  跟踪性能

    Fig.  8  Tracking performance

    图  9  跟踪误差

    Fig.  9  The tracking error

    图  10  自适应更新率 $\hat{\theta}_1$ , $\hat{\theta}_2$

    Fig.  10  Adaptive laws $\hat{\theta}_1$ , $\hat{\theta}_2$

    图  11  控制输入

    Fig.  11  Control inputs

    图  12  切换信号轨迹 $\sigma(t)$

    Fig.  12  Trajectory of the switching signal $\sigma(t)$

  • [1] 李海波, 柴天佑, 赵大勇.混合选别浓密机底流矿浆浓度和流量区间智能切换控制方法.自动化学报, 2014, 40(9):1967-1975 http://www.aas.net.cn/CN/abstract/abstract18467.shtml

    Li Hai-Bo, Chai Tian-You, Zhao Da-Yong. Intelligent switching control of underflow slurry concentration and flowrate intervals in mixed separation thickener. Acta Automatica Sinica, 2014, 40(9):1967-1975 http://www.aas.net.cn/CN/abstract/abstract18467.shtml
    [2] 柴天佑, 张亚军.基于未建模动态补偿的非线性自适应切换控制方法.自动化学报, 2011, 37(7):773-786 http://www.aas.net.cn/CN/abstract/abstract17475.shtml

    Chai Tian-You, Zhang Ya-Jun. Nonlinear adaptive switching control method based on unmodeled dynamics compensation. Acta Automatica Sinica, 2011, 37(7):773-786 http://www.aas.net.cn/CN/abstract/abstract17475.shtml
    [3] Wu J L. Feedback stabilization for multiinput switched nonlinear systems:two subsystems case. IEEE Transactions on Automatic Control, 2008, 53(4):1037-1042 doi: 10.1109/TAC.2008.919518
    [4] Wu J L. Stabilizing controllers design for switched nonlinear systems in strict-feedback form. Automatica, 2009, 45(4):1092-1096 doi: 10.1016/j.automatica.2008.12.004
    [5] Ma R C, Dimirovski G M, Zhao J. Backstepping robust H control for a class of uncertain switched nonlinear systems under arbitrary switchings. Asian Journal of Control, 2013, 15(1):41-50 doi: 10.1002/asjc.v15.1
    [6] 李杰, 齐晓慧, 夏元清, 高志强.线性/非线性自抗扰切换控制方法研究.自动化学报, 2016, 42(2):202-212 http://www.aas.net.cn/CN/abstract/abstract18810.shtml

    Li Jie, Qi Xiao-Hui, Xia Yuan-Qing, Gao Zhi-Qiang. On linear/nonlinear active disturbance rejection switching control. Acta Automatica Sinica, 2016, 42(2):202-212 http://www.aas.net.cn/CN/abstract/abstract18810.shtml
    [7] Long F, Fei S M, Fu Z M, Zheng S Y. Adaptive neural network control for switched system with unknown nonlinear part by using backstepping approach:SISO case. Advances in Neural Networks-ISNN 2006. Berlin Heidelberg:Springer-Verlag, 2006. 842-848
    [8] Han T T, Ge S S, Lee T H. Adaptive neural control for a class of switched nonlinear systems. Systems & Control Letters, 2009, 58(2):109-118
    [9] Li Y M, Tong S C, Li T S. Adaptive fuzzy backstepping control design for a class of pure-feedback switched nonlinear systems. Nonlinear Analysis:Hybrid Systems, 2015, 16(1):72-80
    [10] Jiang B, Shen Q K, Shi P. Neural-networked adaptive tracking control for switched nonlinear pure-feedback systems under arbitrary switching. Automatica, 2015, 61:119-125 doi: 10.1016/j.automatica.2015.08.001
    [11] Ge S S, Wang C. Direct adaptive NN control of a class of nonlinear systems. IEEE Transactions on Neural Networks, 2002, 13(1):214-221 doi: 10.1109/72.977306
    [12] Ma J J, Ge S S, Zheng Z Q, Hu D. Adaptive NN control of a class of nonlinear systems with asymmetric saturation actuators. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(7):1532-1538 doi: 10.1109/TNNLS.2014.2344019
    [13] Wang M, Wang C. Learning from adaptive neural dynamic surface control of strict-feedback systems. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(6):1247-1259 doi: 10.1109/TNNLS.2014.2335749
    [14] He W, Chen Y H, Yin Z. Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Transactions on Cybernetics, 2016, 46(3):620-629 doi: 10.1109/TCYB.2015.2411285
    [15] Li D J. Neural network control for a class of continuous stirred tank reactor process with dead-zone input. Neurocomputing, 2014, 131:453-459 doi: 10.1016/j.neucom.2013.11.006
    [16] Al Janaideh M, Rakheja S, Su C Y. A generalized Prandtl-Ishlinskii model for characterizing the hysteresis and saturation nonlinearities of smart actuators. Smart Materials and Structures, 2009, 18(4):045001 doi: 10.1088/0964-1726/18/4/045001
    [17] Zhao W, Ren X M, Gao X H. Synchronization and tracking control for multi-motor driving servo systems with backlash and friction. International Journal of Robust and Nonlinear Control, 2016, 26(13):2745-2766 doi: 10.1002/rnc.v26.13
    [18] Liu Z, Chen C, Zhang Y. Decentralized robust fuzzy adaptive control of humanoid robot manipulation with unknown actuator backlash. IEEE Transactions on Fuzzy Systems, 2015, 23(3):605-616 doi: 10.1109/TFUZZ.2014.2321591
    [19] 胡洲, 王志胜, 甄子洋.带输入饱和的欠驱动吊车非线性信息融合控制.自动化学报, 2014, 40(7):1522-1527 http://www.aas.net.cn/CN/abstract/abstract18422.shtml

    Hu Zhou, Wang Zhi-Sheng, Zhen Zi-Yang. Nonlinear information fusion control for underactuated cranes with input saturation. Acta Automatica Sinica, 2014, 40(7):1522-1527 http://www.aas.net.cn/CN/abstract/abstract18422.shtml
    [20] Li Y M, Tong S C, Li T S. Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation. Fuzzy Sets and Systems, 2014, 248:138-155 doi: 10.1016/j.fss.2013.11.006
    [21] Wang H Q, Chen B, Liu X P, Liu K F, Liu C. Adaptive neural tracking control for stochastic nonlinear strict-feedback systems with unknown input saturation. Information Sciences, 2014, 269:300-315 doi: 10.1016/j.ins.2013.09.043
    [22] Sui S, Tong S C, Li Y M. Adaptive fuzzy backstepping output feedback tracking control of MIMO stochastic pure-feedback nonlinear systems with input saturation. Fuzzy Sets and Systems, 2014, 254:26-46 doi: 10.1016/j.fss.2014.03.013
    [23] Zhang L L, Yang G H. Dynamic surface error constrained adaptive fuzzy output feedback control for switched nonlinear systems with unknown dead zone. Neurocomputing, 2016, 199:128-136 doi: 10.1016/j.neucom.2016.03.028
    [24] Zhao X D, Shi P, Zheng X L, Zhang L X. Adaptive tracking control for switched stochastic nonlinear systems with unknown actuator dead-zone. Automatica, 2015, 60:193-200 doi: 10.1016/j.automatica.2015.07.022
    [25] Niu B, Li L. Adaptive neural network tracking control for a class of switched strict-feedback nonlinear systems with input delay. Neurocomputing, 2016, 173:2121-2128 doi: 10.1016/j.neucom.2015.10.059
    [26] Tong S C, Sui S, Li Y M. Observed-based adaptive fuzzy tracking control for switched nonlinear systems with dead-zone. IEEE Transactions on Cybernetics, 2015, 45(12):2816-2826 doi: 10.1109/TCYB.2014.2386912
    [27] Yin S, Yu H, Shahnazi R, Haghani A. Fuzzy adaptive tracking control of constrained nonlinear switched stochastic pure-feedback systems. IEEE Transactions on Cybernetics, 2017, 47(3):579-588, DOI: 10.1109/TCYB.2016.2521179
    [28] Wu J, Su B Y, Li J, Zhang X, Ai L F. Global adaptive neural tracking control of nonlinear MIMO systems. Neural Computing and Applications, 2016, DOI: 10.1007/s00521-016-2268-x
    [29] Sanner R M, Slotine J J E. Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks, 1992, 3(6):837-863 doi: 10.1109/72.165588
    [30] Wang H Q, Chen B, Liu K F, et al. Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(5):947-958 doi: 10.1109/TNNLS.2013.2283879
  • 加载中
图(12)
计量
  • 文章访问数:  2377
  • HTML全文浏览量:  302
  • PDF下载量:  844
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-05-04
  • 录用日期:  2016-10-09
  • 刊出日期:  2017-08-20

目录

    /

    返回文章
    返回