一种不完全信息下递推辨识方法及收敛性分析

杜大军; 商立立; 漆波; 费敏锐

doi:10.16383/j.aas.2015.c140766

一种不完全信息下递推辨识方法及收敛性分析

doi: 10.16383/j.aas.2015.c140766

1.
上海大学机电工程与自动化学院上海市电站自动化技术重点实验室上海 200072

基金项目:

国家自然科学基金(61473182), 国家重大科学仪器设备开发专项课题(2012YQ15008703), 上海市青年科技启明星计划(13QA1401600), 上海市科委项目(12JC1404201, 14JC1402200, 15JC1401900)资助

详细信息

作者简介:
杜大军上海大学机电工程与自动化学院副研究员.主要研究方向为网络辨识与网络化先进控制.E-mail:ddj@shu.edu.cn

计量
- 文章访问数: 1515
- HTML全文浏览量: 49
- PDF下载量: 1423
- 被引次数: 0
出版历程
- 收稿日期: 2014-11-06
- 修回日期: 2015-03-20
- 刊出日期: 2015-08-20

Convergence Analysis of an Online Recursive Identification Method with Uncomplete Communication Constraints

1.
Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072

Funds:

Supported by National Natural Science Foundation of China (61473182), National Key Scientific Instrument and Equipment Development Project (2012YQ15008703), Shanghai Rising-Star Program (13QA1401600), and Science and Technology Commission of Shanghai Municipality (12JC1404201, 14JC1402200, 15JC1401900)

摘要

摘要: 针对信号在网络环境下传输带来不完全信息使得在线参数辨识算法和收敛性困难的问题, 不同于传统递推最小二乘方法, 本文提出了一种不完全信息下递推辨识方法并分析其收敛性. 首先运用伯努利分布刻画引起不完全信息的数据丢包特性, 然后基于辅助模型方法补偿不完全信息并构造了新的数据信息矩阵, 并运用矩阵正交变换性质对数据信息矩阵进行QR分解, 推导了融合网络参数的递推辨识新算法, 理论证明了在不完全信息下递推参数辨识算法的收敛性. 最后仿真结果验证了所提方法的可行性和有效性.
- 数据丢包 /
- 参数估计 /
- 递推最小二乘 /
- 矩阵QR分解 /
- 算法收敛性
Abstract: Under the network environment, the uncomplete infromation causes an undesirable effect on the parameter identification and convergence. Unlike the traditional recursive least squares (RLS) algorithm, the paper proposes a novel online recursive identification method with uncomplete communication constraints. In this algorithm, the Bernoulli process is firstly employed to describe the character of data packet losses, and the uncomplete information is compensated by the auxiliary model strategy. The new data information matrix is then constructed, which is decomposed by QR decomposition and the intermediate matrix can be updated recursively. An new recursive least squares (RLS) algorithm under networks with random packet losses is then presented, and its convergence is analysed. Simulation confirms the feasibility and efficiency of the proposed method.
- Data packet dropout /
- parameter estimation /
- recursive least squares /
- matrix QR decomposition /
- algorithm convergence

HTML全文

参考文献(29)

[1]	Aström K J, Kumar P R. Control: a perspective. Automatica, 2014, 50(1): 3-43
[2]	Zhang L X, Gao H J, Kaynak O. Network-induced constraints in networked control systems-a survey. IEEE Transactions on Industrial Informatics, 2013, 9(1): 403-416
[3]	You Ke-You, Xie Li-Hua. Survey of recent progress in networked control systems. Acta Automatica Sinica, 2013, 39(2): 101-118(游科友, 谢立华. 网络控制系统的最新研究综述. 自动化学报, 2013, 39(2): 101-118)
[4]	Du Da-Jun, Fei Min-Rui, Song Yang, Li Xue. Brief survey and prospect of networked control systems. Chinese Journal of Scientific Instrument, 2011, 32(3): 713-720(杜大军, 费敏锐, 宋杨, 李雪. 网络控制系统的简要回顾及展望. 仪器仪表学报, 2011, 32(3): 713-720)
[5]	Wang H L. Task-space synchronization of networked robotic systems with uncertain kinematics and dynamics. IEEE Transactions on Automatic Control, 2013, 58(12): 3169-3174
[6]	Li H Y, Jing X J, Karimi H R. Output-feedback-based H∞ control for vehicle suspension systems with control delay. IEEE Transactions on Industrial Electronics, 2014, 61(1): 436-446
[7]	Zhang X M, Han Q L. Event-triggered dynamic output feedback control for networked control systems. IET Control Theory and Applications, 2014, 8(4): 226-234
[8]	Garcia E, Antsaklis P J. Model-based event-triggered control for systems with quantization and time-varying networks delays. IEEE Transactions on Automatic Control, 2013, 58(2): 422-434
[9]	Zhang W A, Dong H, Guo G, Yu L. Distributed sampled-data H∞ filtering for sensor networks with nonuniform sampling periods. IEEE Transactions on Industrial Informatics, 2014, 10(2): 871-881
[10]	Peng C, Han Q L. A novel event-triggered transmission scheme and L2 control co-design for sampled-data control systems. IEEE Transactions on Automatic Control, 2013, 58(10): 2620-2626
[11]	Wang Le-Yi, Zhao Wen-Xiao. System identification: new paradigms, challenges and opportunities. Acta Automatica Sinica, 2013, 39(7): 933-942(王乐一, 赵文虓. 系统辨识: 新的模式、挑战及机遇. 自动化学报, 2013, 39(7): 933-942)
[12]	Wang J D, Zheng W X, Chen T W. Identification of linear dynamic systems operating in a networked environment. Automatica, 2009, 45(12): 2763-2772
[13]	Irshad Y, Mossberg M, Soderstrom T. System identification in a networked environment using second order statistical properties. Automatica, 2013, 49(2): 652-659
[14]	Fei M R, Du D J, Li K. A fast model identification method for networked control system. Applied Mathematics and Computation, 2008, 205(2): 658-667
[15]	Albertos P, Sanchis R, Sala A. Output prediction under scarce data operation: control applications. Automatica, 1999, 35(10): 1671-1681
[16]	Shi Y, Fang H Z. Kalman filter-based identification for systems with randomly missing measuremetns in a network environment. International Journal of Control, 2010, 83(3): 538-551
[17]	Ding F, Liu G J, Liu X P. Parameter estimation with scarce measurements. Automatica, 2011, 47(8): 1646-1655
[18]	Ni Bo-Yi, Xiao De-Yun. A recursive identification method for non-uniformly sampled systems. Acta Automatica Sinica, 2009, 35(12): 1520-1527(倪博溢, 萧德云. 非均匀采样系统的一种递推辨识方法. 自动化学报, 2009, 35(12): 1520-1527)
[19]	Guo L, Chen H F. Optimal adaptive control and consistent parameter estimates for ARMAX model with quadratic cost. SIAM Journal on Control and Optimization, 1987, 24(4): 845-867
[20]	Guo Lei. Time-varying Stochastic Systems: Stability, Estimation and Control. Changchun: Jilin Science and Technology Press, 1993. (郭雷. 时变随机系统: 稳定性, 估计与控制. 长春: 吉林科技出版社, 1993.)
[21]	Guo L, Chen H F. Asymptotic optimal adaptive control with consistent parameter estimates. SIAM Journal on Control and Optimization, 1987, 25(3): 558-575
[22]	Cioffi J M, Kailath T. Fast, recursive-least-squares transversal filters for adaptive filtering. IEEE Transactions on Acoustics, Speech and Signal Processing, 1984, 32(2): 304-337
[23]	Alexander S T, Ghirnikar A L. A method for recursive least squares filtering based upon an inverse QR decomposition. IEEE Transactions on Signal Processing, 1993, 41(1): 20-30
[24]	Liu Z S. On-line parameter identification algorithms based on Householder transformation. IEEE Transactions on Signal Processing, 1993, 41(9): 2863-2871
[25]	Golub G H, Loan C V. Matrix computation. Baltimor: Johns Hopkins Press.
[26]	Ding F, Chen T W. Combined parameter and output estimation of dual-rate systems using an auxiliary model. Automatica, 2004, 40(10): 1739-1748
[27]	Ding Feng, Yang Jia-Ben. Remarks on martingale hyperconvergence theorem and the convergence analysis of the forgetting factor least squares algorithm. Control Theory and Applications, 1999, 16(4): 569-572(丁峰, 杨家本. 关于鞅超收敛定理与遗忘因子最小二乘算法的收敛性分析. 控制理论与应用, 1999, 16(4): 569-572)
[28]	Dasgupta S, Huang Y F. Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor for systems with bounded noise. IEEE Transactions on Information Theory, 1987, 33(3): 383-392
[29]	Guo L, Ljung L, Priouret P. Performance analysis of the forgetting factor RLS algorithm. International Journal of Adaptive Control and Signal Processing, 1993, 7(6): 525-537