State estimation using extended Kalman filters with application to vehicle dynamics control | |
| Author | Boonserm Kaewkamnerdpong |
| Call Number | AIT Thesis no. ISE-02-01 |
| Subject(s) | Vehicles--Models Kalman filtering |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. ISE-02-01 |
| Abstract | In the last decade, the automotive control systems have reached a critical level of complexity. A number of separate control units are used to facilitate functions like Antilock- braking, Drive-slip-control, Advanced Cruise Control, Anti-skid/yaw-control and Active Suspension. The next stage is to configure the driving dynamics of cars using Drive-by-wire concepts, or Control-configured vehicles, which requires unified control approaches with a high degree of interaction between the individual control systems. To accomplish this with good control performance, it is crucially necessary to estimate the unknown or partially known states of the vehicle in an accurate and reliable way with a minimum number of sensors. Since the observability of a non-linear dynamic system depends on the state and input vectors, there are some driving situations where observation is restricted. Moreover, the sensor reliability is also dependent upon driving situations. Hence, the state estimation should be able to deal with different situations in different ways. This work proposes a state estimation method using extended Kalman filters applied to the vehicle control system based on a rule-based system which is used to tune the covariance of Kalman filter due to different possible driving situations. To verify the idea, this estimation method is applied to a vehicle control system both in simulation and the real system. The experiment is done concentrated on the straightforward manoeuvres, which are some of the critical driving situations due to lack of information. However, to prove that this idea can be used in the system as it was before, the experiment is done also in the curving and cornering manoeuvres. The results are better since the Kalman filter is tuned to behave in the manner that improve the estimation efficiency for the current driving situation. |
| Year | 2002 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. ISE-02-01 |
| Type | Thesis |
| School | School of Advanced Technologies (SAT) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Manukid Parnichkun; |
| Examination Committee(s) | Afzulpurkar, Nitin V.;Bohez, Erik L. J.; |
| Scholarship Donor(s) | Siemens, AG.; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2002 |