An Accurate and Generic Testing Approach to Vehicle Stability Parameters Based on GPS and INS

With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. As a common method, usually GPS sensors and INS sensors are applied to measure vehicle stability parameters by fusing data from the two system sensors. Although prior model parameters should be recognized in a Kalman filter, it is usually used to fuse data from multi-sensors. In this paper, a robust, intelligent and precise method to the measurement of vehicle stability is proposed. First, a fuzzy interpolation method is proposed, along with a four-wheel vehicle dynamic model. Second, a two-stage Kalman filter, which fuses the data from GPS and INS, is established. Next, this approach is applied to a case study vehicle to measure yaw rate and sideslip angle. The results show the advantages of the approach. Finally, a simulation and real experiment is made to verify the advantages of this approach. The experimental results showed the merits of this method for measuring vehicle stability, and the approach can meet the design requirements of a vehicle stability controller.

[1]  Toshiki Matsui,et al.  Measurement of Vehicle Sideslip Angle Using Stereovision , 2005 .

[2]  Zhang Hongtian,et al.  Vehicle stability control system based on direct measurement of body sideslip angle , 2009, 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS).

[3]  Hui Chen,et al.  Review on Vehicle Sideslip Angle Estimation , 2013 .

[4]  Robert Shorten,et al.  A methodology for the design of robust rollover prevention controllers for automotive vehicles: Part 2-Active steering , 2007, ACC.

[5]  James F. Whidborne,et al.  Ideal Vehicle Sideslip Estimation Using Consumer Grade GPS and INS , 2009 .

[6]  Peter Teunissen,et al.  Array-based satellite phase bias sensing: theory and GPS/BeiDou/QZSS results , 2014 .

[7]  Jianqiu Li,et al.  Combined AFS and DYC Control of Four-Wheel-Independent-Drive Electric Vehicles over CAN Network with Time-Varying Delays , 2014, IEEE Transactions on Vehicular Technology.

[8]  Julien Caroux,et al.  SIDESLIP ANGLE MEASUREMENT, EXPERIMENTAL CHARACTERIZATION AND EVALUATION OF THREE DIFFERENT PRINCIPLES , 2007 .

[9]  Patrick Gruber,et al.  Comparison of Feedback Control Techniques for Torque-Vectoring Control of Fully Electric Vehicles , 2014, IEEE Transactions on Vehicular Technology.

[10]  Xiaoyu Huang,et al.  Robust Sideslip Angle Estimation for Lightweight Vehicles Using Smooth Variable Structure Filter , 2013 .

[11]  Anne Laurent,et al.  Multi-Core Parallel Gradual Pattern Mining Based on Multi-Precision Fuzzy Orderings , 2013, Algorithms.

[12]  Rajesh Rajamani,et al.  GPS-based real-time identification of tire-road friction coefficient , 2002, IEEE Trans. Control. Syst. Technol..

[13]  Ali Charara,et al.  Experimental evaluation of a sliding mode observer for tire-road forces and an extended Kalman filter for vehicle sideslip angle , 2007, 2007 46th IEEE Conference on Decision and Control.

[14]  Robert Odolinski,et al.  Combined GPS and BeiDou Instantaneous RTK Positioning , 2014 .

[15]  Hongtian Zhang,et al.  Fuzz interpolation in GPS/INS data fusion , 2010, The 2010 IEEE International Conference on Information and Automation.

[16]  Hyeongcheol Lee,et al.  New adaptive approaches to real-time estimation of vehicle sideslip angle , 2009 .

[17]  David M. Bevly,et al.  Integrating INS Sensors With GPS Measurements for Continuous Estimation of Vehicle Sideslip, Roll, and Tire Cornering Stiffness , 2006, IEEE Transactions on Intelligent Transportation Systems.

[18]  Bálint Vanek,et al.  Aircraft trajectory tracking with large sideslip angles for sense and avoid intruder state estimation , 2014, 22nd Mediterranean Conference on Control and Automation.

[19]  Shu-Chung Yi,et al.  Feature-Based Vehicle Flow Analysis and Measurement for a Real-Time Traffic Surveillance System , 2012, J. Inf. Hiding Multim. Signal Process..

[20]  Flavio Nardi,et al.  Vehicle Sideslip Angle Estimation and Experimental Validation , 2013 .

[21]  Antonella Ferrara,et al.  Integral Sliding Mode for the Torque-Vectoring Control of Fully Electric Vehicles: Theoretical Design and Experimental Assessment , 2015, IEEE Transactions on Vehicular Technology.

[22]  Ali Charara,et al.  Experimental evaluation of tire-road forces and sideslip angle observers , 2007, 2007 European Control Conference (ECC).

[24]  Tianzhen Liu,et al.  Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence Theory , 2014, Algorithms.

[25]  J. Christian Gerdes,et al.  Validating GPS Based Measurements for Vehicle Control , 2005 .

[26]  Hongtian Zhang,et al.  A fuzz application in GPS/INS navigation , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[27]  Jeng-Shyang Pan,et al.  Vision-based Vehicle Forward Collision Warning System Using Optical Flow Algorithm , 2015, J. Inf. Hiding Multim. Signal Process..

[28]  Beatriz L. Boada,et al.  Sideslip angle estimator based on ANFIS for vehicle handling and stability , 2015 .

[29]  Mohammed Chadli,et al.  Moment robust output controller to improve vehicle stability , 2009, 2009 European Control Conference (ECC).

[30]  Hiroshi Fujimoto,et al.  Electric vehicle stability control based on disturbance accommodating Kalman filter using GPS , 2013, 2013 IEEE International Conference on Mechatronics (ICM).

[31]  Kyongsu Yi,et al.  An investigation into differential braking strategies for vehicle stability control , 2003 .

[32]  Hongtian Zhang,et al.  Data Fusion Modeling for an RT3102 and Dewetron System Application in Hybrid Vehicle Stability Testing , 2015, Algorithms.