Distributed robust vehicle state estimation

A distributed estimation approach based on opinion dynamics is proposed to enhance the reliability of vehicle corners' velocity estimates. The corners' estimates, which are obtained from a Kalman filter, is formed by integrating the model-based and kinematic-based velocity estimation approaches. These estimates are utilized as opinions with different levels of confidence in the developed algorithm. More reliable estimates robust to disturbances and time delay are achieved via solving a convex optimization problem. Vehicle tests with various driveline configurations are performed to verify the estimator performance under different surfaces friction conditions in pure and combined-slip (combination of longitudinal/lateral) maneuvers, which are arduous for the current vehicle state estimators.

[1]  Amir Khajepour,et al.  Estimation of longitudinal speed robust to road conditions for ground vehicles , 2016 .

[2]  Liang Tong.  An Approach for Vehicle State Estimation Using Extended Kalman Filter , 2012 .

[3]  Tobias Ortmaier,et al.  State estimation of vehicle's lateral dynamics using unscented Kalman filter , 2014, 53rd IEEE Conference on Decision and Control.

[4]  Amir Khajepour,et al.  Robust Estimation and Experimental Evaluation of Longitudinal Friction Forces in Ground Vehicles , 2014 .

[5]  Rudolph van der Merwe,et al.  Efficient derivative-free Kalman filters for online learning , 2001, ESANN.

[6]  Shreyas Sundaram,et al.  Coherence and convergence rate in networked dynamical systems , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[7]  Ali Charara,et al.  Observers for vehicle tyre/road forces estimation: experimental validation , 2010 .

[8]  Robert F. Stengel,et al.  Optimal Control and Estimation , 1994 .

[9]  Huei Peng,et al.  A Cost-Effective Sideslip Estimation Method Using Velocity Measurements From Two GPS Receivers , 2014, IEEE Transactions on Vehicular Technology.

[10]  Yong Xu,et al.  A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations , 2014 .

[11]  Carlos Canudas-de-Wit,et al.  Dynamic tyre friction models for combined longitudinal and lateral vehicle motion , 2005 .

[12]  John A. Grogg,et al.  Algorithms for Real-Time Estimation of Individual Wheel Tire-Road Friction Coefficients , 2006, IEEE/ASME Transactions on Mechatronics.

[13]  S. Glaser,et al.  Vehicle lateral dynamics estimation using unknown input proportional-integral observers , 2006, 2006 American Control Conference.

[14]  Xuemin Shen,et al.  Connected Vehicles: Solutions and Challenges , 2014, IEEE Internet of Things Journal.

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

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

[17]  Huei Peng,et al.  Robust Vehicle Sideslip Angle Estimation Through a Disturbance Rejection Filter That Integrates a Magnetometer With GPS , 2014, IEEE Transactions on Intelligent Transportation Systems.

[18]  J. Christian Gerdes,et al.  Integrating Inertial Sensors With Global Positioning System (GPS) for Vehicle Dynamics Control , 2004 .

[19]  M. Buslowicz,et al.  Simple stability criterion for a class of delay differential systems , 1987 .

[20]  B. Anderson,et al.  Detectability and Stabilizability of Time-Varying Discrete-Time Linear Systems , 1981 .

[21]  Matt C. Best,et al.  Comparison between Kalman Filter and Robust Filter for Vehicle Handling Dynamics State Estimation , 2002 .

[22]  Tor Arne Johansen,et al.  Vehicle velocity estimation using nonlinear observers , 2006, Autom..

[23]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[24]  Sean N. Brennan,et al.  Lateral Vehicle State and Environment Estimation Using Temporally Previewed Mapped Lane Features , 2015, IEEE Transactions on Intelligent Transportation Systems.

[25]  Bernard Delyon,et al.  A note on uniform observability , 2001, IEEE Trans. Autom. Control..

[26]  Yan Wang,et al.  Lateral Tire Force Estimation With Unknown Input Observer , 2012 .

[27]  Magnus Egerstedt,et al.  Graph Theoretic Methods in Multiagent Networks , 2010, Princeton Series in Applied Mathematics.

[28]  Rajesh Rajamani,et al.  Algorithms for Real-Time Estimation of Individual Wheel Tire-Road Friction Coefficients , 2012 .

[29]  Joachim Rudolph,et al.  Nonlinear observer design for state estimation during antilock braking , 2014, J. Syst. Control. Eng..

[30]  Carlos Canudas-de-Wit,et al.  Dynamic Friction Models for Road/Tire Longitudinal Interaction , 2003 .

[31]  Vibhor L. Bageshwar,et al.  Stochastic Observability Test for Discrete-Time Kalman Filters , 2009 .

[32]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[33]  Amir Khajepour,et al.  Integrated estimation structure for the tire friction forces in ground vehicles , 2016, 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[34]  Amir Khajepour,et al.  A comprehensive study on the stability analysis of vehicle dynamics with pure/combined-slip tyre models , 2016 .

[35]  Keith J. Burnham,et al.  Dual extended Kalman filter for vehicle state and parameter estimation , 2006 .