State Estimation By Observer Using Median Operation for Observed Output with Outliers

A design method for a state estimation observer for observed outputs with outliers is proposed in this paper. Outliers are one of the most important problems to be solved in actual systems control, such as engine valve control in automobiles and motion control using camera devices. When outliers occur in output signals, the output data for the control system becomes invalid. The state estimation observer does not work well because of this invalid value. Therefore, the effect of the outliers needs to be overcome for implementing state feedback control using state estimators. Accordingly, we propose a method to remove the effect of the outliers for the estimated state using median operation. The effectiveness of the observer based on the median of candidate vectors is verified by numerical simulation.

[1]  Kiminao Kogiso,et al.  Attack Detection and Prevention for Encrypted Control Systems by Application of Switching-Key Management , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[2]  Yasuaki Kaneda,et al.  Robust State Estimation Observer Using Median for Outliers and Data-lost , 2019 .

[3]  Kenji Sugimoto,et al.  State Estimation via Switching Observer for Systems with Outliers , 2011 .

[4]  M. Yamakita,et al.  Design method of robust Kalman filter via ℓ1 regression and its application for vehicle control with outliers , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[5]  James Richard Forbes,et al.  Gradient-Based Observer for Simultaneous Localization and Mapping , 2018, IEEE Transactions on Automatic Control.

[6]  Simo Särkkä,et al.  Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations , 2009, IEEE Transactions on Automatic Control.

[7]  Yoichi Hori,et al.  Advanced Motion Control of Electric Vehicles Based on Robust Lateral Tire Force Control via Active Front Steering , 2014, IEEE/ASME Transactions on Mechatronics.

[8]  Masaki Yamakita,et al.  Design method of robust Kalman filter for multi output systems based on statistics , 2013, 2013 American Control Conference.

[9]  Kazuma Sekiguchi,et al.  Vehicle localization by sensor fusion of LRS measurement and odometry information based on moving horizon estimation , 2014, 2014 IEEE Conference on Control Applications (CCA).

[10]  Kenji Sawada,et al.  Optimal quantization interval design of dynamic quantizers which satisfy the communication rate constraints , 2010, 49th IEEE Conference on Decision and Control (CDC).

[11]  Yuki Minami,et al.  Dynamic quantizer design for MIMO systems based on communication rate constraint , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[12]  Chang-Hua Lien,et al.  Robust observer-based control of systems with state perturbations via LMI approach , 2004, IEEE Transactions on Automatic Control.

[13]  S. Mitter,et al.  Robust Recursive Estimation in the Presence of Heavy-Tailed Observation Noise , 1994 .

[14]  Stefan Schaal,et al.  A Kalman filter for robust outlier detection , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Yuki Minami,et al.  Dynamic quantizer design for MIMO systems based on communication rate constraint , 2013 .

[16]  Hiroshi Fujimoto,et al.  Driving Force Distribution and Control for EV With Four In-Wheel Motors: A Case Study of Acceleration on Split-Friction Surfaces , 2017, IEEE Transactions on Industrial Electronics.

[17]  Kenji Sawada,et al.  Integrated Design of Filter and Interval in Dynamic Quantizer under Communication Rate Constraint , 2011 .

[18]  Koichi Suyama Reliable observer-based control using vector-valued decision by majority , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[19]  Guang-Hong Yang,et al.  Secure Switched Observers for Cyber-Physical Systems Under Sparse Sensor Attacks: A Set Cover Approach , 2019, IEEE Transactions on Automatic Control.

[20]  Hyungbo Shim,et al.  Detection of Sensor Attack and Resilient State Estimation for Uniformly Observable Nonlinear Systems having Redundant Sensors , 2018, IEEE Transactions on Automatic Control.

[21]  D. Luenberger An introduction to observers , 1971 .

[22]  Lili Wang,et al.  A Distributed Observer for a Time-Invariant Linear System , 2016, IEEE Transactions on Automatic Control.

[23]  Kenji Sawada,et al.  Dynamic Quantizer Design Under Communication Rate Constraints , 2016, IEEE Transactions on Automatic Control.

[24]  Sophie Tarbouriech,et al.  Time-Varying Sampled-Data Observer With Asynchronous Measurements , 2019, IEEE Transactions on Automatic Control.

[25]  Kunihisa Okano,et al.  Stabilization of uncertain systems with finite data rates and Markovian packet losses , 2013, 2013 European Control Conference (ECC).

[26]  Chia-Chi Tsui On the Order Reduction of Linear Functional Observers , 1985 .