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基于遗忘因子的数据驱动最优迭代学习控制算法研究
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TP273

基金项目:

国家自然科学基金（61873139）；山东省泰山学者青年专家人才工程；山东省自然科学基金（ZR2019MF036）


Forgetting factor based data-driven optimal iterative learning control
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    摘要:

    针对一类有限时间内重复运行的非线性非仿射离散时间系统，本文提出了一种基于遗忘因子的数据驱动最优迭代学习控制方法.首先，引入一种迭代动态线性化方法，将被控非线性系统等效化为线性输入输出增量形式；其次，分析了最优迭代学习控制方法中存在的问题，并针对由历史信息的累积效应所导致的控制输入不能及时响应的问题，设计自适应遗忘因子使算法具有更好的可控性和灵活性.所提出的控制方法是数据驱动的控制方法，设计和分析过程仅依赖于系统的输入输出数据，不包含任何显式模型信息.最后，通过仿真验证了该方法的有效性.

    Abstract:

    A forgetting factor based data-driven optimal iterative learning control method is proposed for a class of nonlinear nonaffine discrete-time systems running repeatedly in finite time.First, an iterative dynamic linearization method is introduced to transform the nonlinear system into a linear input and output incremental form.Second, the main problems of the optimal iterative learning method are analyzed.To solve the problem that the control input cannot respond in time due to the accumulation effect of historical information, an adaptive forgetting factor is designed to make the method more controllable and flexible.The proposed control method is a data-driven control approach, and the design and analysis process only depends on the input and output data of the system and does not contain any explicit model information.Finally, the effectiveness of the proposed method is verified by simulation results.

    参考文献
    [1] Arimoto S,Kawamura S,Miyazaki F.Bettering operation of robots by learning[J].Journal of Robotic Systems,1984,1(2):123-140
    [2] Yu Q X,Hou Z S,Xu J X.D-type ILC based dynamic modeling and norm optimal ILC for high-speed trains[J].IEEE Transactions on Control Systems Technology,2018,26(2):652-663
    [3] Zhang H W,Yu F S,Bu X H,et al.Robust iterative learning control for permanent magnet linear motor[J].Electric Machines and Control,2012,16(6):81-86
    [4] Tan K K,Zhao S,Xu J X.Online automatic tuning of a proportional integral derivative controller based on an iterative learning control approach[J].IET Control Theory & Applications,2007,1(1):90-96
    [5] Adloo H,Deghat M,Karimaghaee P.Iterative state feedback control and its application to robot control[C]//IEEE International Conference on Mechatronics,2009:1-6
    [6] Xu W K,Chu B,Rogers E.Cascade based iterative learning control of robotic-assisted upper extremity stroke rehabilitation[C]//IEEE 52nd Annual Conference on Decision and Control (CDC),2013:6688-6693
    [7] Meng D Y,Jia Y M,Du J P,et al.On iterative learning algorithms for the formation control of nonlinear multi-agent systems[J].Automatica,2014,50(1):291-295
    [8] Shao Z,Xiang Z R.Adaptive iterative learning control for switched nonlinear continuous-time systems[J].International Journal of Systems Science,2019,50(5):1028-1038
    [9] Lu J Y,Cao Z X,Gao F R.Multipoint iterative learning model predictive control[J].IEEE Transactions on Industrial Electronics,2019,66(8):6230-6240
    [10] Liu S D,Wang J R,Shen D,et al.Iterative learning control for noninstantaneous impulsive fractional-order systems with varying trial lengths[J].International Journal of Robust and Nonlinear Control,2018,28(18):6202-6238
    [11] Mannion A F,Brox J I,Fairbank J C T.Comparison of spinal fusion and nonoperative treatment in patients with chronic low back pain:long-term follow-up of three randomized controlled trials[J].The Spine Journal,2013,13(11):1438-1448
    [12] Annamalai A S K,Sutton R,Yang C,et al.Robust adaptive control of an uninhabited surface vehicle[J].Journal of Intelligent & Robotic Systems,2015,78(2):319-338
    [13] Woo J,Kim N.Vision based obstacle detection and collision risk estimation of an unmanned surface vehicle[C]//International Conference on Ubiquitous Robots & Ambient Intelligence,2016:461-465
    [14] Uchiyama M.Formation of high-speed motion pattern of a mechanical arm by trial[J].Transactions of the Society of Instrument and Control Engineers,1978,14(6):706-712
    [15] Kaneko O.On linear canonical controllers within the unfalsified control framework[J].IFAC Proceedings Volumes,2008,41(2):12279-12284
    [16] Tanaskovic M,Fagiano L,Novara C,et al.Data-driven control of nonlinear systems:an on-line direct approach[J].Automatica,2017,75:1-10
    [17] Hou Z S,Chi R H,Gao H J.An overview of dynamic-linearization-based data-driven control and applications[J].IEEE Transactions on Industrial Electronics,2017,64(5):4076-4090
    [18] Wang D,Liu D R,Zhang Q C,et al.Data-based adaptive critic designs for nonlinear robust optimal control with uncertain dynamics[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2016,46(11):1544-1555
    [19] Janssens P,Pipeleers G,Swevers J.A data-driven constrained norm-optimal iterative learning control framework for LTI systems[J].IEEE Transactions on Control Systems Technology,2013,21(2):546-551
    [20] Chi R H,Hou Z S,Jin S T,et al.An improved data-driven point-to-point ILC using additional on-line control inputs with experimental verification[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2019,49(4):687-696
    [21] Chi R H,Hou Z S,Huang B,et al.A unified data-driven design framework of optimality-based generalized iterative learning control[J].Computers & Chemical Engineering,2015,77(9):10-23
    [22] Radac M B,Precup R E.Model-free constrained data-driven iterative reference input tuning algorithm with experimental validation[J].International Journal of General Systems,2016,45(4):455-476
    [23] Chi R H,Hou Z S,Jin S T,et al.Computationally efficient data-driven higher order optimal iterative learning control[J].IEEE Transactions on Neural Networks and Learning Systems,2018,29(12):5971-5980
    [24] Chi R H,Hou Z S,Jin S T,et al.Computationally-light non-lifted data-driven norm-optimal iterative learning control[J].Asian Journal of Control,2018,20(1):115-124
    [25] Bu X H,Yu Q X,Hou Z S,et al.Model free adaptive iterative learning consensus tracking control for a class of nonlinear multiagent systems[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2019,49(4):677-686
    [26] Lin N,Chi R H,Huang B,et al.Multi-lagged-input iterative dynamic linearization based data-driven adaptive iterative learning control[J].Journal of the Franklin Institute,2019,356(1):457-473
    [27] Chi R H,Lin N,Zhang R K,et al.Stochastic high-order internal model-based adaptive TILC with random uncertainties in initial states and desired reference points[J].International Journal of Adaptive Control and Signal Processing,2017,31(5):726-741
    [28] Chi R H,Huang B,Hou Z S,et al.Data-driven high-order terminal iterative learning control with a faster convergence speed[J].International Journal of Robust and Nonlinear Control,2018,28(1):103-119
    [29] Chi R H,Hou Z S,Jin S T,et al.Computationally-light non-lifted data-driven norm-optimal iterative learning control[J].Asian Journal of Control,2018,20(1):115-124
    [30] Chi R H,Zhang R K,Feng Y J,et al.Data-driven optimal terminal iterative learning control with initial value dynamic compensation[J].IET Control Theory & Applications,2016,10(12):1357-1364
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李佳伟,林娜,池荣虎.基于遗忘因子的数据驱动最优迭代学习控制算法研究[J].南京信息工程大学学报(自然科学版),2021,13(5):582-588
LI Jiawei, LIN Na, CHI Ronghu. Forgetting factor based data-driven optimal iterative learning control[J]. Journal of Nanjing University of Information Science & Technology, 2021,13(5):582-588

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历史
  • 收稿日期:2020-08-31
  • 在线发布日期: 2021-12-02

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