Sideslip angle estimator based on ANFIS for vehicle handling and stability

Most of the existing ESC (Electronic stability control) systems rely on the measurement of both yaw rate and sideslip angle. However, one of the main issues is that the sideslip angle cannot be measured directly because the sensors are too expensive. For this reason, sideslip angle estimation has been widely discussed in literature. The modeling of sideslip angle is complex due to the non-linear dynamics of the vehicle. This work proposes a new methodology based on ANFIS to estimate the vehicle sideslip angle. The estimator has been validated by comparing the proposed ANFIS prediction model with the values provided by CARSIM model, which is an experimentally validated software. The advantage of this estimation is the modeling of the non-linear dynamics of the vehicle by means of signals which are directly measured from vehicle sensors. The results show the effectiveness of the proposed ANFIS-based sideslip angle estimator.

[1]  María Jesús López Boada,et al.  Integrated control of front-wheel steering and front braking forces on the basis of fuzzy logic , 2006 .

[2]  Aleksander B. Hac,et al.  Estimation of Vehicle Roll Angle and Side Slip for Crash Sensing , 2010 .

[3]  María Jesús López Boada,et al.  Fuzzy-logic applied to yaw moment control for vehicle stability , 2005 .

[4]  Gianpiero Rocca,et al.  A Neural-Network-Based Model for the Dynamic Simulation of the Tire/Suspension System While Traversing Road Irregularities , 2008, IEEE Transactions on Neural Networks.

[5]  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.

[6]  Ali Charara,et al.  Estimation of vehicle sideslip, tire force and wheel cornering stiffness , 2009 .

[7]  Edoardo Sabbioni,et al.  On the vehicle sideslip angle estimation through neural networks: Numerical and experimental results , 2011 .

[8]  Rajesh Rajamani,et al.  Development and Experimental Evaluation of a Slip Angle Estimator for Vehicle Stability Control , 2006, IEEE Transactions on Control Systems Technology.

[9]  P.P. Cruz,et al.  Vector control using ANFIS controller with space vector modulation [induction motor drive applications] , 2004, 39th International Universities Power Engineering Conference, 2004. UPEC 2004..

[10]  Kyongsu Yi,et al.  Design and evaluation of side slip angle-based vehicle stability control scheme on a virtual test track , 2006, IEEE Transactions on Control Systems Technology.

[11]  Yu Yao,et al.  Robust Control: Theory and Applications , 2016 .

[12]  J. Jancirani,et al.  Reducing the seat vibration of vehicle by semi active force control technique , 2014 .

[13]  Moustafa El-Gindy,et al.  Possible application of artificial neural networks to vehicle dynamics and control: a literature review , 1993, International Journal of Vehicle Design.

[14]  María Jesús López Boada,et al.  Modeling of a magnetorheological damper by recursive lazy learning , 2011 .

[15]  Konghui Guo,et al.  The UniTire model: a nonlinear and non-steady-state tyre model for vehicle dynamics simulation , 2005 .

[16]  Saied Taheri,et al.  Application of Recursive Least Square Algorithm on Estimation of Vehicle Sideslip Angle and Road Friction , 2010 .

[17]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[18]  Wanzhong Zhao,et al.  Study on soft computing arithmetic for vehicle yaw rate based on ANFIS , 2011 .

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

[20]  Weiwen Deng,et al.  RLS-based online estimation on vehicle linear sideslip , 2006, 2006 American Control Conference.

[21]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[22]  Jeonghoon Song,et al.  Development and comparison of integrated dynamics control systems with fuzzy logic control and sliding mode control , 2013, Journal of Mechanical Science and Technology.

[23]  Nong Zhang,et al.  Robust vehicle stability control based on sideslip angle estimation , 2011 .

[24]  Ali Charara,et al.  Virtual sensor: application to vehicle sideslip angle and transversal forces , 2004, IEEE Transactions on Industrial Electronics.

[25]  Kihong Park,et al.  Controller design for improving lateral vehicle dynamic stability , 2001 .

[26]  Ali Charara,et al.  Onboard Real-Time Estimation of Vehicle Lateral Tire–Road Forces and Sideslip Angle , 2011, IEEE/ASME Transactions on Mechatronics.

[27]  Jianbo Lu,et al.  Robust sideslip estimation using GPS road grade sensing to replace a pitch rate sensor , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[28]  Bo-Chiuan Chen,et al.  Sideslip angle estimation using extended Kalman filter , 2008 .

[29]  Huei Peng,et al.  A study on lateral speed estimation methods , 2004 .

[30]  Lin Chuan,et al.  Virtual Sensor for Vehicle Sideslip Angle Based on Extended Kalman Filter , 2011, 2011 Third International Conference on Measuring Technology and Mechatronics Automation.

[31]  Jimoh O. Pedro,et al.  Direct adaptive neural control of antilock braking systems incorporated with passive suspension dynamics , 2012 .

[32]  Jinxiang Wang,et al.  Design and evaluation of sideslip angle observer for vehicle stability control , 2011 .

[33]  Zhuoping Yu,et al.  Sideslip angle estimation based on input–output linearisation with tire–road friction adaptation , 2010 .