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Real-time states estimation of a farm tractor using dynamic mode decomposition

文献类型： 外文期刊

作者： Wang, Hao 1 ; Noguchi, Noboru 2 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Hokkaido Univ, Res Fac Agr, Sapporo, Hokkaido 0658589, Japan

关键词： System identification; Machine learning control; Total least squares; Regression model; Vehicle dynamics

期刊名称：GPS SOLUTIONS （ 影响因子：4.066； 五年影响因子：4.373 ）

ISSN： 1080-5370

年卷期： 2020 年 25 卷 1 期

页码：

收录情况： SCI

摘要： We present a pure data-driven method to estimate vehicle dynamics from the measurements of sideslip and yaw rate in the use of GPS and inertial navigation system. The GPS and INS configuration provides vehicle position, velocity vector, vehicle orientation, and yaw rate observations. A new dynamic mode decomposition with control (DMDc) method denoises the state observations by adopting the total least-squares algorithm. The total least-squares DMD with control (tlsDMDc) helps discover the underlying dynamics with the time-dependent observations of states and external control. The experiments of a simulated linear dynamic model with synthetic Gaussian noise illustrate that the solutions of tlsDMDc are more accurate than the standard DMDc to characterize underlying dynamics with imperfect measurements. We additionally investigate how the algorithm performs on vehicle motion deduction and sensor bias correction. It has been shown that the tlsDMDc-based state estimator with the couple of GPS and inertial sensor measurements provides accurate and robust observation in the presence of model error and measurement noise.

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