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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ray, L.R. |
| Copyright Year | 1995 |
| Description | Author affiliation: Dept. of Mech. Eng., Christian Brothers Univ., Memphis, TN, USA (Ray, L.R.) |
| Abstract | This paper presents methods for estimating road coefficient of friction (/spl mu/) in real time using an extended Kalman filter (EKF) and Bayesian decision making. The EKF estimates the motion and tire forces of an eight degree-of-freedom vehicle based on vehicle-mounted sensors. The filter requires no a priori knowledge of /spl mu/ and does not require a tire force model. The resulting tire force, slip, and slip angle estimates are compared statistically with those that result from a nominal tire model to select the most likely coefficient of friction from a set of hypothesized values. The /spl mu/ identification and EKF tasks are separate; therefore, EKF state estimates can be used for feedback control while /spl mu/ is identified, /spl mu/ identification results can be used for IVHS decision making and for determining controller setpoints. Simulation results show excellent convergence and accuracy of the /spl mu/ estimates. Computation and sensor requirements, and robustness of the /spl mu/ identification algorithm are considered. |
| Starting Page | 2133 |
| Ending Page | 2137 |
| File Size | 586444 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780324455 |
| DOI | 10.1109/ACC.1995.531275 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1995-06-21 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Roads Friction Tires Decision making Force sensors Bayesian methods Motion estimation Vehicles Filters State estimation |
| Content Type | Text |
| Resource Type | Article |
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