Correction of contaminated yaw rate signal and estimation of sensor bias for an electric vehicle under normal driving conditions
| Autor: | Guoguang Zhang, Zitian Yu, Junmin Wang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering business.product_category Aerospace Engineering 02 engineering and technology 020901 industrial engineering & automation Robustness (computer science) Control theory Electric vehicle 0202 electrical engineering electronic engineering information engineering medicine Simulation Civil and Structural Engineering Yaw-rate sensor Signal reconstruction Ground business.industry Mechanical Engineering 020208 electrical & electronic engineering Yaw Stiffness Motion control Computer Science Applications Control and Systems Engineering Signal Processing medicine.symptom business |
| Zdroj: | Mechanical Systems and Signal Processing. 87:64-80 |
| ISSN: | 0888-3270 |
| DOI: | 10.1016/j.ymssp.2016.05.034 |
| Popis: | Yaw rate is a crucial signal for the motion control systems of ground vehicles. Yet it may be contaminated by sensor bias. In order to correct the contaminated yaw rate signal and estimate the sensor bias, a robust gain-scheduling observer is proposed in this paper. First of all, a two-degree-of-freedom (2DOF) vehicle lateral and yaw dynamic model is presented, and then a Luenberger-like observer is proposed. To make the observer more applicable to real vehicle driving operations, a 2DOF vehicle model with uncertainties on the coefficients of tire cornering stiffness is employed. Further, a gain-scheduling approach and a robustness enhancement are introduced, leading to a robust gain-scheduling observer. Sensor bias detection mechanism is also designed. Case studies are conducted using an electric ground vehicle to assess the performance of signal correction and sensor bias estimation under difference scenarios. |
| Databáze: | OpenAIRE |
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