scholarly journals Real-time FPGA-based Kalman filter for constant and non-constant velocity periodic error correction

2017 ◽  
Vol 48 ◽  
pp. 133-143 ◽  
Author(s):  
Chen Wang ◽  
Ethan D. Burnham-Fay ◽  
Jonathan D. Ellis
Keyword(s):  
Kalman Filter ◽  
Error Correction ◽  
Real Time ◽  
Constant Velocity ◽  
Periodic Error

Bench-top setup for validation of real time, digital periodic error correction

2006 ◽  
Vol 30 (3) ◽  
pp. 306-313 ◽  
Author(s):  
Tony L. Schmitz ◽  
Lonnie Houck ◽  
David Chu ◽  
Lee Kalem
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Periodic Error

scholarly journals A Kinematic Error Controller for Real-Time Kinematic Error Correction of Industrial Robots

2021 ◽  
Vol 53 ◽  
pp. 705-715
Author(s):  
Mitchell R. Woodside ◽  
Joseph Fischer ◽  
Patrick Bazzoli ◽  
Douglas A. Bristow ◽  
Robert G. Landers
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Industrial Robots ◽  
Kinematic Error ◽  
Real Time Kinematic

scholarly journals Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 750
Author(s):  
Wenkang Wan ◽  
Jingan Feng ◽  
Bao Song ◽  
Xinxin Li
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
State Parameter ◽  
State Observation ◽  
Straight Line ◽  
Distributed Drive Electric Vehicle ◽  
Huber Method ◽  
Yaw Moment

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

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