US 12,377,861 B2
Method and system for vehicle sideslip angle estimation based on event-triggered state estimation
Lei Zhang, Beijing (CN); Zhenpo Wang, Beijing (CN); and Xiaolin Ding, Beijing (CN)
Assigned to BEIJING INSTITUTE OF TECHNOLOGY, Beijing (CN)
Appl. No. 18/015,020
Filed by BEIJING INSTITUTE OF TECHNOLOGY, Beijing (CN)
PCT Filed Jun. 1, 2021, PCT No. PCT/CN2021/097607
§ 371(c)(1), (2) Date Jan. 6, 2023,
PCT Pub. No. WO2022/166046, PCT Pub. Date Aug. 11, 2022.
Claims priority of application No. 202110167355.X (CN), filed on Feb. 5, 2021.
Prior Publication US 2023/0264700 A1, Aug. 24, 2023
Int. Cl. B60W 40/12 (2012.01); B60W 50/00 (2006.01)
CPC B60W 40/12 (2013.01) [B60W 50/00 (2013.01); B60W 2050/0031 (2013.01); B60W 2050/0052 (2013.01); B60W 2520/12 (2013.01); B60W 2520/14 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A method for vehicle sideslip angle estimation based on event-triggered state estimation, comprising:
setting zk+1=[xk+ωzIMU·T; ωzIMU] as an observation variable, and adopting a Kalman filtering algorithm for iteration aiming at estimation of an estimated vehicle yaw rate, wherein ωzIMU is a vehicle yaw rate measured by an Inertial Measurement Unit (IMU), T is a sampling cycle, xk is an estimated state variable obtained in a kth iteration of the Kalman filtering algorithm;
in the iteration:
when (zk−zτk−1)T(zk−zτk−1)<δ, εk is set to 1, otherwise εk is set to 0, wherein δ is a preset threshold value;
when εk is 0, a state variable is estimated according to xk+1=xk+1|k+(1−εk+1)Kk+1(zk+1−zk+1|k)+εk+1Mk+1 (zrk−zk+1|k), and a vehicle sideslip angle is calculated according to the estimated vehicle yaw rate obtained by the Kalman filtering algorithm, wherein

OG Complex Work Unit Math
γGPS is a Global Positioning System (GPS) heading angle that is measured by a GPS, H is an observation matrix Pk+1|k=APkA′+Q, A is a state-transition matrix, A′ is a rank transformation of the state-transition matrix, H′ is a rank transformation of the observation matrix, Q is a system noise covariance, Pk is an error covariance, R is an observation noise covariance, I is a unit matrix, τ1 is a first predefined parameter, and τ2 is a second predefined parameter, wherein τ1=1 and τ2=1;
when εk is 1, it is determined whether the GPS heading angle is updated; and when the GPS heading angle is updated, the state variable is estimated according to xk+1=xk+1|k+(1−εk+1)Kk+1(zk+1−zk+1|k)+εk+1Mk+1 (zrk−zk+1|k) and the vehicle sideslip angle is calculated according to the estimated vehicle yaw rate obtained by the Kalman filtering algorithm; and when the GPS heading angle is not updated yet, the iteration is terminated, and the vehicle sideslip angle is estimated by fusing measurement data of both the GPS and the IMU;
controlling a vehicle according to the vehicle sideslip angle.