A method of online traction parameter identification and mapping

Kobelski, Alexander; Osinenko, Pavel; Streif, Stefan

doi:10.1016/j.ifacol.2020.12.909

Cited by 3 publications

(4 citation statements)

References 20 publications

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“…In the last part, the design and setup of validation experiments is described. Model equations are derived in a similar fashion as in Kobelski et al (2020). Consider the wheel force diagram of Fig.…”

Section: Methodsmentioning

confidence: 99%

“…For similar ground types the three parameters p, α 1 , α 2 may be fixed, while only the parameter a can be varied to cover a spectrum of ground conditions -see Fig. 5 in (Kobelski et al, 2020). The µ(s)-characteristics for each soil type can partially be obtained from literature, even before operation.…”

Section: Adhesion Slip Ratio Characteristic Modelmentioning

confidence: 99%

“…The input vector includes the drive torques, front vertical force F zf and longitudinal component of the drawbar pull: In (Kobelski et al, 2020) observability of the overall system as well as possibilities to measure or estimate M d , F zf and F dx are discussed. The tire deformation rolling resistance coefficients ρ t are assumed as fixed parameters.…”

Section: Adaptive Unscented Kalman Filtermentioning

confidence: 99%

“…In the following, we outline briefly the approach. First of all, in a previous work (Kobelski et al, 2020) an UKF-based identification algorithm was used to estimate traction properties of a tractor driving over different ground types in a simulation. It was based on the methodology developed in (Osinenko et al, 2014(Osinenko et al, , 2016Osinenko and Streif, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Experimental verification of an online traction parameter identification method

Kobelski

Osinenko

Streif

2021

Control Engineering Practice

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Section: Methodsmentioning

confidence: 99%

Section: Adhesion Slip Ratio Characteristic Modelmentioning

confidence: 99%

Section: Adaptive Unscented Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental verification of an online traction parameter identification method

Kobelski

Osinenko

Streif

2021

Control Engineering Practice

Self Cite

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Aligning process quality and efficiency in agricultural soil tillage

Kazenwadel,

Becker,

Graf

et al. 2023

At - Automatisierungstechnik

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Automation in agricultural machinery is a crucial driver of productivity and sustainability. Some automation features like automated steering and real-time data analytics are already state-of-the-art. On the other hand, a human driver performs the optimization of the working speed manually, and the automation of this is an ongoing challenge. Process quality and process efficiency are the two main targets in this optimization. Agricultural soil tillage requires achieving both. Therefore, the correlation between process quality optimization and process efficiency is fundamental, and vice versa. The approach presented in this paper shows how the two optimization targets of efficiency and process quality can be optimized and aligned together. Optical sensors determine various parameters to describe and model the process quality. The measured machine state determines the characteristics of the interaction forces between the machine and the environment. A machine learning algorithm describes the relationships in the drivetrain. The two process targets are each predicted for different working speeds and are combined in the form of a boundary target and an optimization target to identify one optimized target speed value.

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