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

未建模动态增量补偿驱动的非线性PID控制及应用

张亚军 魏萃 柴天佑 卢绍文 崔东亮

张亚军, 魏萃, 柴天佑, 卢绍文, 崔东亮. 未建模动态增量补偿驱动的非线性PID控制及应用. 自动化学报, 2020, 46(6): 1145−1153 doi: 10.16383/j.aas.c190146
引用本文: 张亚军, 魏萃, 柴天佑, 卢绍文, 崔东亮. 未建模动态增量补偿驱动的非线性PID控制及应用. 自动化学报, 2020, 46(6): 1145−1153 doi: 10.16383/j.aas.c190146
Zhang Ya-Jun, Wei Cui, Chai Tian-You, Lu Shao-Wen, Cui Dong-Liang. Un-modeled dynamics increment compensation driven nonlinear PID control and its application. Acta Automatica Sinica, 2020, 46(6): 1145−1153 doi: 10.16383/j.aas.c190146
Citation: Zhang Ya-Jun, Wei Cui, Chai Tian-You, Lu Shao-Wen, Cui Dong-Liang. Un-modeled dynamics increment compensation driven nonlinear PID control and its application. Acta Automatica Sinica, 2020, 46(6): 1145−1153 doi: 10.16383/j.aas.c190146

未建模动态增量补偿驱动的非线性PID控制及应用

doi: 10.16383/j.aas.c190146
基金项目: 国家自然科学基金(61773107, 61603168, 61866021, 61890924, 61833004, 61991402, 61473107), 流程工业综合自动化国家重点实验室开放基金(PAL-N201808)资助
详细信息
    作者简介:

    张亚军:东北大学讲师. 主要研究方向为非线性模糊自适应控制理论, 广义预测控制, 多模型切换控制, 智能解耦控制, 数据驱动控制, 智能控制系统的大数据建模, 工业过程大数据建模及其应用.E-mail: yajunzhang@mail.neu.edu.cn

    魏萃:东北大学流程工业综合自动化国家重点实验室博士研究生. 主要研究方向为非线性控制, 机器人. 本文通信作者.E-mail: weicui@stumail.neu.edu.cn

    柴天佑:中国工程院院士, 东北大学教授. IEEE Fellow, IFAC Fellow, 欧亚科学院院士. 主要研究方向为自适应控制, 智能解耦控制, 流程工业综合自动化理论、方法与技术.E-mail: tychai@mail.neu.edu.cn

    卢绍文:东北大学流程工业综合自动化国家重点实验室教授. 主要研究方向为工业过程建模与仿真. 目前主要研究多尺度随机建模方法和可视化方法.E-mail: lusw@mail.neu.edu.cn

    崔东亮:东北大学讲师. 主要研究方向为多目标优化, 列车调度优化, 数据分析.E-mail: cuidongliang@mail.neu.edu.cn

Un-modeled Dynamics Increment Compensation Driven Nonlinear PID Control and Its Application

Funds: Supported by National Natural Science Foundation of China (61773107, 61603168, 61866021, 61890924, 61833004, 61991402, 61473107), and State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201808)
  • 摘要: 针对一类具有强非线性、机理不清且动态特性随不同运行条件而变化的复杂过程, 将基于数据的建模技术与基于模型的控制策略相结合, 提出了未建模动态及其未知增量补偿驱动的非线性PID控制方法. 所提的算法将一步超前最优控制策略应用于PID控制器的参数设计, 并结合非线性补偿技术进行综合设计, 从理论上给出了PID控制器参数以及非线性补偿器设计的一般原则和方法, 为解决传统PID控制器参数难于整定的问题提供了方法和途径. 在此基础上, 分析了闭环系统的稳定性和收敛性. 最后, 将所提的控制算法进行数值仿真实验以及Pendubot系统平衡控制的对比实验, 实验结果表明, 在Pendubot的精确摩擦力模型未知的情况下, 所提算法能有效地消除系统未知时变不确定性的影响, 并尽可能地减少Pendubot摆角的波动, 将摆角控制在规定的目标值范围内.
  • 图  1  本文控制方法与文献[30]控制方法的仿真结果

    Fig.  1  Simulation results of the control method in [30] and the proposed method

    图  2  Pendubot系统实验平台

    Fig.  2  The experimental platform of the Pendubot system

    图  3  实验结果

    Fig.  3  Experimental results

    表  1  性能评价

    Table  1  Performance indexes

    绝对误差累积和 误差均方差
    文献[30] 23 396.5 2.7
    本文方法 8 156.1 1.8
    下载: 导出CSV

    表  2  性能评价

    Table  2  Performance indexes

    绝对误差累积和 误差均方差
    常规PD 361.1 6.5
    文献[30] 337.3 6.1
    本文方法 204.3 4.2
    下载: 导出CSV
  • [1] 赵大勇, 柴天佑. 再磨过程泵池液位区间与给矿压力模糊切换控制. 自动化学报, 2013, 39(5): 556−564

    Zhao Da-Yong, Chai Tian-You. Fuzzy switching control for sump level interval and hydrocyclone pressure in regrinding process. Acta Automatica Sinica, 2013, 39(5): 556−564
    [2] 贾瑶, 张立岩, 柴天佑. 矿浆中和过程中基于模型预估模糊自适应控制. 东北大学学报, 2014, 35(5): 617−621 doi: 10.3969/j.issn.1005-3026.2014.05.003

    Jia Yao, Zhang Li-Yan, Chai Tian-You. Based on fuzzy adaptive control of model predictive in slurry neutralization process. Journal of Northeastern University Natural Science, 2014, 35(5): 617−621 doi: 10.3969/j.issn.1005-3026.2014.05.003
    [3] Zhang Y J, Jia Y, Chai T Y, Wang D H, Dai W, Fu J. Data-driven PID controller and its application to pulp neutralization process. IEEE Transactions on Control Systems Technology, 2018, 26(3): 828−841 doi: 10.1109/TCST.2017.2695981
    [4] Xia D Y, Chai T Y, Wang L Y. Fuzzy neural-network friction compensation-based singularity avoidance energy swing-up to nonequilibrium unstable position control of Pendubot. IEEE Transactions on Control Systems Technology, 2014, 22(2): 690−705 doi: 10.1109/TCST.2013.2255290
    [5] 魏萃, 柴天佑, 贾瑶, 王良勇. 补偿信号法驱动的Pendubot自适应平衡控制. 自动化学报, 2019, 45(6): 1146−1156

    Wei Cui, Chai Tian-You, Jia Yao, Wang Liang-Yong. Compensation signal driven adaptive balance control of the Pendubot. Acta Automatica Sinica, 2019, 45(6): 1146−1156
    [6] Chen L, Narendra K S. Nonlinear adaptive control using neural networks and multiple models. Automatica, 2001, 37(8): 1245−1255 doi: 10.1016/S0005-1098(01)00072-3
    [7] Fu Y, Chai T Y. Nonlinear multivariable adaptive control using multiple models and neural networks. Automatica, 2007, 43(8): 1101−1110
    [8] 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, 2010, 37(7): 773−786

    Chai Tian-You, Zhang Ya-Jun. Nonlinear adaptive switching control method based on un-modeled dynamics compensation. Acta Automatica Sinica, 2010, 37(7): 773−786
    [9] Wang Y G, Chai T Y, Fu J, Zhang Y J, Fu Y. Adaptive decoupling switching control based on generalized predictive control. IET Control Theory and Application, 2012, 12(6): 1−12
    [10] Wang Y G, Chai T Y, Fu J, Sun J, Wang H. Adaptive decoupling switching control of the forced-circulation evaporation system using neural networks. IEEE Transactions on Control Systems Technology, 2013, 21(3): 964−974 doi: 10.1109/TCST.2012.2193883
    [11] Hou Z S, Jin S T. Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems. IEEE Transactions on Neural Networks, 2011, 22(12): 2173−2188 doi: 10.1109/TNN.2011.2176141
    [12] Zhu Y M, Hou Z S. Data-driven MFAC for a class of discrete-time nonlinear systems with RBFNN. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(5): 1013−1020
    [13] Dai W, Chai T Y, Yang S X. Data-driven optimization control for safety operation of hematite grinding process. IEEE Transactions on Industrial Electronics, 2015, 62(5): 2930−2941 doi: 10.1109/TIE.2014.2362093
    [14] Chi R H, Liu Y, Hou Z S, Jin S T. Data-driven terminal iterative learning control with high-order learning law for a class of non-linear discrete-time multiple-input–multiple output systems. IET Control Theory and Applications, 2015, 9(7): 1075−1082
    [15] Chai T Y, Zhang Y J, Wang H, Su C Y, Sun J. Data-based virtual un-modeled dynamics driven multivariable nonlinear adaptive switching control. IEEE Transactions on Neural Networks, 2011, 22(12): 2154−2171 doi: 10.1109/TNN.2011.2167685
    [16] Spong M W, Block D J. The Pendubot: A mechatronic system for control research and education. In: Proceedings of the 34th IEEE Conference on Decision and Control.New Orleans, LA, USA: IEEE, 1995. 555−556
    [17] Zhang M J, Tzyh-Jong T. Hybrid control of the Pendubot. IEEE/ASME Transactions on Mechatronics, 2002, 7(1): 79−86 doi: 10.1109/3516.990890
    [18] Xin X, Liu Y N. Reduced-order stable controllers for two- link underactuated planar robots. Automatica, 2013, 49(7): 2176−2183 doi: 10.1016/j.automatica.2013.03.027
    [19] Sanchez E N, Flores V. Real-time fuzzy PI+PD control for an underactuated robot. In: Proceedings of the 2002 IEEE Internatinal Symposium on Intelligent Control. Vancouver, BC, Canada: IEEE, 2002. 137−141
    [20] 侯俊, 王良勇, 柴天佑, 方正. 基于T-S模糊的欠驱动机械臂的平衡控制. 控制工程, 2012, 19(1): 5−8, 85 doi: 10.3969/j.issn.1671-7848.2012.01.002

    Hou Jun, Wang Liang-Yong, Chai Tian-You, Fang Zheng. Balance control of underactuated manipulator using T-S fuzzy scheme. Control Engineering of China, 2012, 19(1): 5−8, 85 doi: 10.3969/j.issn.1671-7848.2012.01.002
    [21] Wang W, Yi J Q, Zhao D B, Liu X J. Adaptive sliding mode controller for an underactuated manipulator. In: Proceedings of the 2004 International Conference on Machine Learning and Cybernetics. Shanghai, China: IEEE, 2004. 882−887
    [22] Spall J C, Cristion J A. Model-free control of nonlinear stochastic systems with discrete-time measurements. IEEE Transactions on Automatic Control, 1998, 43(9): 1198−1210 doi: 10.1109/9.718605
    [23] Hjalmarsson H, Gevers M, Gunnarsson S, Lequin O. Iterative feedback tuning: Theory and applications. IEEE Control Systems Magazine, 1998, 18(4): 26−41 doi: 10.1109/37.710876
    [24] Agnoloni T, Mosca E. Controller falsification based on multiple models. International Journal of Adaptive Control and Signal Processing, 2003, 17(2): 163−177 doi: 10.1002/acs.745
    [25] Safonov M G, Tsao T C. The unfalsified control concept and learning. IEEE Transactions on Automatic Control, 1997, 42(6): 843−847 doi: 10.1109/9.587340
    [26] Campi M C, Lecchini A, Savaresi S M. Virtual reference feedback tuning: A direct method for the design of feedback controllers. Automatica, 2002, 38(8): 1337−1346 doi: 10.1016/S0005-1098(02)00032-8
    [27] Markovsky I, Rapisarda P. Data-driven simulation and control. International Journal of Control, 2008, 81(12): 1946−1959 doi: 10.1080/00207170801942170
    [28] Jang J S R. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on System, Man, Cybernetics, 1993, 23(3): 665−685 doi: 10.1109/21.256541
    [29] Zhang Y J, Chai T Y, Wang D H. An alternating identification algorithm for a class of nonlinear dynamical systems. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(7): 1606−1617 doi: 10.1109/TNNLS.2016.2547968
    [30] Eom M, Chwa D. Robust swing-up and balancing control using a nonlinear disturbance observer for the Pendubot system with dynamic friction. IEEE Transactions on Robotics, 2015, 31(2): 331−343 doi: 10.1109/TRO.2015.2402512
    [31] Sun N, Fang Y C, Chen H, Lu B, Fu Y M. Slew/Translation positioning and swing suppression for 4-DOF tower cranes with parametric uncertainties: Design and hardware experimentation. IEEE Transactions on Industrial Electronics, 2016, 63(10): 6407−6418
    [32] 王永富, 柴天佑. 一种补偿动态摩擦的自适应模糊控制方法. 中国电机工程学报, 2005, 25(2): 139−143

    Wang Yong-Fu, Chai Tian-You. Adaptive fuzzy control method for dynamic friction compensation. Proceedings of the CSEE, 2005, 25(2): 139−143
  • 加载中
图(3) / 表(2)
计量
  • 文章访问数:  1828
  • HTML全文浏览量:  155
  • PDF下载量:  352
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-03-07
  • 录用日期:  2019-06-09
  • 网络出版日期:  2020-07-10
  • 刊出日期:  2020-07-10

目录

    /

    返回文章
    返回