基于MIT规则的自适应扩展集员估计方法

宋大雷; 吴冲; 齐俊桐; 韩建达

doi:10.3724/SP.J.1004.2012.01847

基于MIT规则的自适应扩展集员估计方法

doi: 10.3724/SP.J.1004.2012.01847

1.
中国科学院沈阳自动化研究所机器人学国家重点实验室沈阳 110016;
2.
中国科学院大学北京 100049

详细信息

通讯作者:
齐俊桐

计量
- 文章访问数: 1618
- HTML全文浏览量: 37
- PDF下载量: 760
- 被引次数: 0
出版历程
- 收稿日期: 2011-11-18
- 修回日期: 2012-01-10
- 刊出日期: 2012-11-20

A MIT-based Nonlinear Adaptive Set-membership Filter for Ellipsoidal Estimation

1.
State Key Libratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016;
2.
University of Chinese Academy of Sciences, Beijing 100049

摘要

摘要: 用于非线性椭球估计的自适应扩展集员(Adaptive extended set-membership filter, AESMF)算法在实际应用中存在着过程噪声设定椭球与真实噪声椭球失配的问题, 导致滤波器的估计出现偏差甚至发散. 本文提出了一种基于MIT规则过程噪声椭球最优化的自适应扩展集员估计算法(MIT-AESMF), 用于解决非线性系统时变状态和参数的联合估计和定界中过程噪声无法精确建模问题的新算法. 本算法通过MIT优化规则,在线计算使一步预测偏差包络椭球最小化的过程噪声包络椭球, 以此保证滤波器健康指标满足有效条件; 最后, 采用地面移动机器人状态和动力学参数联合估计验证了所提出方法的有效性.
- 扩展集员估计 /
- MIT自适应规则 /
- 自适应滤波 /
- 参数定界
Abstract: The adaptive extended set-membership filter (AESMF) for nonlinear ellipsoidal estimation suffers the mismatch between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method-based adaptive set-membership filter for optimization of the set boundaries of process noise is developed and applied to nonlinear joint estimation of both time-varying states and parameters. As a result of using the proposed MIT, the estimation stability and boundaries accuracy of conventional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method.
- Extended set-membership filter /
- MIT adaptive strategy /
- adaptive filter /
- parameter determination

HTML全文

参考文献(1)

[1]

Haykin S, de Freitas N. Special issue on sequential state estimation. Proceedings of the IEEE, 2004, 92(3): 399-400[2] Ahrens J H, Khalil H K. Closed-loop behavior of a class of nonlinear systems under EKF-based control. IEEE Transactions on Automatic Control, 2007, 52(3): 536-540[3] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation. Proceedings of IEEE, 2004, 92(3) 401-422[4] Song Q, Jiang Z, Han J D. UKF-based active model and adaptive inverse dynamics control for mobile robot. In: Proceedings of IEEE International Conference on Robotics and Automation. Roma, Italy: IEEE, 2007. 4164-4169[5] Salvatore L, Stasi S, Tarchioni L. A new EKF-based algorithm for flux estimation in induction machines. IEEE Transactions on Industrial Electronics, 1993, 40(5): 496-504[6] Julier S, Uhlmann J, Durrant-Whyte H F. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 2000, 45(3): 477-482[7] Ljung L. Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Transactions on Automatic Control, 1979, 24(1): 36-50[8] Jiang Z, Song Q, He Y Q, Han J D. A novel adaptive unscented Kalman filter for nonlinear estimation. In: Proceedings of the 46th IEEE Conference on Decision and Control, 2007, New Orleans, LA, USA: IEEE, 2007. 4293-4298[9] Peng Y, Han J D, Huang Q J. Adaptive UKF based tracking control for unmanned trimaran vehicles. International Journal of Innovative Computing, Information and Control, 2009, 5(10): 3505-3516[10] Rao A K, Huang Y F. Analysis of finite precision effects on a recursive set membership parameter estimation algorithm. IEEE Transactions on Signal Processing, 1992, 40(12): 3081-3085[11] Schweppe F. Recursive state estimation: unknown but bounded errors and system inputs. IEEE Transactions on Automatic Control, 1968, 13(1): 22-28[12] Bertsekas D, Rhodes I. Recursive state estimation for a set-membership description of uncertainty. IEEE Transactions on Automatic Control, 1971, 16(2): 117-128[13] Fogel E, Huang Y F. On the value of information in system identification-bounded noise case. Automatica, 1982, 18(2): 229-238[14] Maksarov D G, Norton J P. State bounding with ellipsoidal set description of the uncertainty. International Journal of Control, 1996, 65(5): 847-866[15] Kurzhanski A B, Varaiya P. Ellipsoidal techniques for reachability analysis: internal approximation. Systems and Control Letters, 2000, 41(3): 201-211[16] Chernousko F L. Ellipsoidal state estimation for dynamical systems. Nonlinear Analysis: Theory, Methods and Applications, 2005, 63(5-7): 872-879[17] Polyak B T, Nazin S A, Durieu C, Walter E. Ellipsoidal parameter or state estimation under model uncertainty. Automatica, 2004, 40(7): 1171-1179[18] Scholte E, Campbell M E. A nonlinear set-membership filter for on-line applications. International Journal of Robust and Nonlinear Control, 2003, 13(15): 1337-1358[19] Zhou B, Han J D, Liu G J. A UD factorization-based nonlinear adaptive set-membership filter for ellipsoidal estimation. International Journal of Robust and Nonlinear Control, 2008, 18(16): 1513-1531[20] Zhou Bo, Han Jian-Da. A UD factorization-based adaptive extended set-membership filter. Acta Automatica Sinica, 2008, 34(2): 150-158 (周波, 韩建达. 基于UD分解的自适应扩展集员估计方法. 自动化学报, 2008, 34(2): 150-158)[21] Zhou B, Han J D. Nonlinear estimation methods for autonomous tracked vehicle with slip. Chinese Journal of Mechanical Engineering, 2007, 20(4): 1-7[22] Dierks T, Jagannathan S. Output feedback control of a quadrotor UAV using neural networks. IEEE Transactions on Neural Networks, 2010, 21(1): 50-66[23] Song Da-Lei, Qi Jun-Tong, Han Jian-Da, Wang Yue-Chao. Model identification and active modeling control for rotor fly-robot: theory and experiment. Acta Automatica Sinica, 2011, 37(4): 480-495 (宋大雷, 齐俊桐, 韩建达, 王越超. 旋翼飞行机器人系统建模与主动模型控制理论及实验研究. 自动化学报, 2011, 37(4): 480-495)[24] Zhao J, Zhu L, Zang X Z, Liu G F, Liu G. Dynamics analysis of a novel mine disaster searching robot. Applied Mechanics and Materials, 2011, 39: 363-368[25] Gu D Z, Walker D K, Randa J. Variable termination unit for noise-parameter measurement. IEEE Transactions on Instrumentation and Measurement, 2009, 58(4): 1072-1077[26] Song Q, Han J D. An adaptive UKF algorithm for the state and parameter estimations of a mobile robot. Acta Automatica Sinica, 2008, 34(1): 72-79

施引文献

资源附件(0)

访问统计