scholarly journals Adaptive Kalman Filter with L2 Feedback Control for Active Suspension Using a Novel 9-DOF Semi-Vehicle Model

Actuators ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 267
Author(s):  
Huan Yang ◽  
Jiang Liu ◽  
Min Li ◽  
Xilong Zhang ◽  
Jianze Liu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Feedback Control ◽  
Control Algorithm ◽  
Active Suspension ◽  
Adaptive Kalman Filter ◽  
Feedback Matrix ◽  
Kalman Filter Algorithm ◽  
Feedback Algorithm ◽  
Suspension Model ◽  
Rear Tire

In order to further improve driving comfort, this paper takes the semi-vehicle active suspension as the research object. Furthermore, combined with a 5-DOF driver-seat model, a new 9-DOF driver seat-active suspension model is proposed. The adaptive Kalman filter combined with L2 feedback control algorithm is used to improve the controller. First, a discrete 9-DOF driver seat-active suspension model is established. Then, the L2 feedback algorithm is used to solve the optimal feedback matrix of the model, and the adaptive Kalman filter algorithm is used to replace the linear Kalman filter. Finally, the improved active suspension model and algorithm are verified through simulation and test. The results show that the new algorithm and model not only significantly improve the driver comfort, but also comprehensively optimize the other performance of the vehicle. Compared with the traditional LQG control algorithm, the RMS value of the acceleration experienced by the driver’s limb are, respectively, decreased by 10.9%, 15.9%, 6.4%, and 7.5%. The RMS value of pitch angle acceleration experienced by the driver decreased by 6.4%, and the RMS value of the dynamic tire deflection of front and rear tire decreased by 32.6% and 12.1%, respectively.

Research on Adaptive Kalman Filter for INS

2017 ◽  
Vol 872 ◽  
pp. 316-320
Author(s):  
Kai Xia Wei
Keyword(s):  
Kalman Filter ◽  
Response Speed ◽  
Measured Data ◽  
Specific Method ◽  
Alignment Error ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Noise Interference ◽  
Kalman Filter Algorithm ◽  
The Stability

Due to sensor accuracy and noise interference and other reasons, the measured data may be inaccurate or even wrong. This will reduce the accuracy of the filter and the reliability and response speed of the Kalman filter, and even make the Kalman filter lose the stability. In this paper, a new INS initial alignment error model and observation model are derived for the errors in INS initial alignment. The adaptive Kalman filter is built to improve the stability and the accuracy of filter. The specific method is to make the adaptive Kalman filter manage to correct the filter online by getting the observed data. The simulation results show the proposed algorithm improves the accuracy of initial alignment in SINS, and prove the adaptive Kalman filter is effective. The main innovation in this paper is to manage to build the adaptive Kalman filter to modify the filter online by using the observed data. The adaptive Kalman filter algorithm improves the accuracy of the filter.

Research on the Decoupling Control Algorithm of Full Vehicle Semi-Active Suspension

2012 ◽  
Vol 479-481 ◽  
pp. 1355-1360
Author(s):  
Jian Guo Chen ◽  
Jun Sheng Cheng ◽  
Yong Hong Nie
Keyword(s):  
Differential Geometry ◽  
Control Algorithm ◽  
Active Suspension ◽  
Suspension System ◽  
Decoupling Control ◽  
Vehicle Suspension ◽  
Full Vehicle ◽  
Vibration Attenuation ◽  
Magneto Rheological ◽  
Suspension Model

Vehicle suspension is a MIMO coupling nonlinear system; its vibration couples that of the tires. When magneto-rheological dampers are adopted to attenuate vibration of the sprung mass, the damping forces of the dampers need to be distributed. For the suspension without decoupling, the vibration attenuation is difficult to be controlled precisely. In order to attenuate the vibration of the vehicle effectively, a nonlinear full vehicle semi-active suspension model is proposed. Considering the realization of the control of magneto-rheological dampers, a hysteretic polynomial damper model is adopted. A differential geometry approach is used to decouple the nonlinear suspension system, so that the wheels and sprung mass become independent linear subsystems and independent to each other. A control rule of vibration attenuation is designed, by which the control current applied to the magneto-rheological damper is calculated, and used for the decoupled suspension system. The simulations show that the acceleration of the sprung mass is attenuated greatly, which indicates that the control algorithm is effective and the hysteretic polynomial damper model is practicable.

Application of Adaptive Kalman Filter Technique in Initial Alignment of Single-Axial Rotation Strap-Down Inertial Navigation System

2012 ◽  
Vol 466-467 ◽  
pp. 617-621
Author(s):  
Song Tian Shang ◽  
Wen Shao Gao
Keyword(s):  
Kalman Filter ◽  
Axial Rotation ◽  
Estimation Accuracy ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Rotation System ◽  
Kalman Filter Algorithm ◽  
Axis Rotation ◽  
Single Axis Rotation ◽  
Better Than

In order to improve the accuracy of initial alignment which determines the accuracy of navigation, a Sage-Husa adaptive kalman filter algorithm is applied to SINS initial alignment of single-axis rotation system. The simulation result further shows that in the case of inaccurate statistical property of noise, the estimation accuracy of Sage-Husa adaptive kalman filter is better than the conventional kalman filter.

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