Combined State Estimation and Active Fault Detection of Individual-Wheel-Drive Vehicles: An Adaptive Observer-Based Approach 2015-01-1107
This paper presents an adaptive observer-based approach for the combined state estimation and active fault detection and isolation (FDI) of the individual-wheel-drive (IWD) vehicles. A 3-DOF vehicle model coupled with the Highway Safety Research Institute (HSRI) tire model is established and used as the observation model. Based on this model, the dual unscented Kalman filter (DUKF) technique is employed for the observer design to give fusion results of the interdependent state and parameter variables, which undergo nonlinear transformations, with the minimum square errors. Effectiveness of the proposed algorithm is examined and validated through co-simulation between MATLAB/Simulink and CarSim. The results demonstrate that the DUKF-based observer effectively filters the sensor signals, accurately obtains the longitudinal and lateral velocities, explicitly isolates the faulty wheel(s) and accurately estimates the actual torque(s) even with the presence of noise.
Citation: Song, P., Zong, C., and Tomizuka, M., "Combined State Estimation and Active Fault Detection of Individual-Wheel-Drive Vehicles: An Adaptive Observer-Based Approach," SAE Technical Paper 2015-01-1107, 2015, https://doi.org/10.4271/2015-01-1107. Download Citation
Author(s):
Pan Song, Changfu Zong, Masayoshi Tomizuka
Affiliated:
Jilin Univ, Univ of California
Pages: 8
Event:
SAE 2015 World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Fault detection
CAD, CAM, and CAE
Mathematical models
Sensors and actuators
SAE MOBILUS
Subscribers can view annotate, and download all of SAE's content.
Learn More »