| Summary: | 碩士 === 國立交通大學 === 機械工程系所 === 104 === In order to reduce the time consumption caused by partial differential equations derived by human when applying Extended Kalman Filter (EKF) to high order nonlinear observers, Dr. Ling-Yuan Hsu brings up a novel observer construction method, 「Switching Observer」. If the system is observable, we could reduce partial differential equations by separating the system into two sub-systems. Reducing equations is not only release the loading of engineers but also influence estimated time and estimated precision of the computation system. This thesis will build a switching observer system with a twenty states high order vehicle model on a single digital processor (DSP) to do real-time simulations.
In the reality, it’s difficult for people to find the center of gravity (C.G) on vehicle and set the sensors on it. We find that the velocity and acceleration measurements of sensors on C.G position are different from non-C.G position because of the vehicle spin. How it influence the observer will also be discussed in this thesis.
The results indicate that the switching observer could save 10.787% of computation time than conventional observer when applying on a high order vehicle model. In this case, the velocity and acceleration measurements caused by spin are much smaller than original velocity and acceleration. Therefore, it will not strongly impact the result of observer.
|
|---|
