Details
| Original language | English |
|---|---|
| Pages (from-to) | 14306-14311 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 53 |
| Issue number | 2 |
| Publication status | Published - 2020 |
| Event | 21st IFAC World Congress 2020 - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020 |
Abstract
Driver assistance systems have become an indispensable part of today's vehicles technology. Especially in the commercial vehicle sector, the challenges in obtaining information increase with rising system complexity. Compared to trucks, trailers for commercial vehicle combinations are sparsely equipped with electronic components. This leads to difficulties in implementation of intelligent systems for the trailer as necessary information is not provided. Reasons for this can be an insufficient sensor equipment due to uneconomical costs or a missing communication channel between the two vehicle units, preventing the transmission of required truck related information to the trailer. A possible model-based method to obtain unmeasured states is the Extended Kalman Filter. However, this approach requires elaborate preliminary work steps of high complexity and a sophisticated domain knowledge. Alternatively, this paper proposes the applicability of Neural Networks for estimating the required state and input variables, namely the articulation angle and the truck's steering angle. Two different network types are used: the Feedforward Neural Network and the Nonlinear Autoregressive Exogenous Neural Network. The measured input variables for the networks, in accordance with the inputs of the Extended Kalman Filter in a previous publication, are merely trailer yaw rate and longitudinal speed. In conclusion, a comparison between the results of the Neural Networks and those of the Extended Kalman Filter is drawn.
Keywords
- Extended kalman filter, Feedforward neural network, Nonlinear autoregressive exogenous neural network, State and input estimation, Truck-Semitrailer combination
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: IFAC-PapersOnLine, Vol. 53, No. 2, 2020, p. 14306-14311.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Neural Observer for Nonlinear State and Input Estimation in a Truck-Semitrailer Combination
AU - Jahn, Tim
AU - Ziaukas, Zygimantas
AU - Kobler, Jan Philipp
AU - Wielitzka, Mark
AU - Ortmaier, Tobias
N1 - ACKNOWLEDGEMENTS The authors of the Institute of Mechatronic Systems would like to thank BPW Bergische Achsen KG for enabling the cooperative project SimTrailer. Additionally, we would like to thank all the BPW staff helping to accomplish the drive tests, particularly the road test team in Brüchermühle.
PY - 2020
Y1 - 2020
N2 - Driver assistance systems have become an indispensable part of today's vehicles technology. Especially in the commercial vehicle sector, the challenges in obtaining information increase with rising system complexity. Compared to trucks, trailers for commercial vehicle combinations are sparsely equipped with electronic components. This leads to difficulties in implementation of intelligent systems for the trailer as necessary information is not provided. Reasons for this can be an insufficient sensor equipment due to uneconomical costs or a missing communication channel between the two vehicle units, preventing the transmission of required truck related information to the trailer. A possible model-based method to obtain unmeasured states is the Extended Kalman Filter. However, this approach requires elaborate preliminary work steps of high complexity and a sophisticated domain knowledge. Alternatively, this paper proposes the applicability of Neural Networks for estimating the required state and input variables, namely the articulation angle and the truck's steering angle. Two different network types are used: the Feedforward Neural Network and the Nonlinear Autoregressive Exogenous Neural Network. The measured input variables for the networks, in accordance with the inputs of the Extended Kalman Filter in a previous publication, are merely trailer yaw rate and longitudinal speed. In conclusion, a comparison between the results of the Neural Networks and those of the Extended Kalman Filter is drawn.
AB - Driver assistance systems have become an indispensable part of today's vehicles technology. Especially in the commercial vehicle sector, the challenges in obtaining information increase with rising system complexity. Compared to trucks, trailers for commercial vehicle combinations are sparsely equipped with electronic components. This leads to difficulties in implementation of intelligent systems for the trailer as necessary information is not provided. Reasons for this can be an insufficient sensor equipment due to uneconomical costs or a missing communication channel between the two vehicle units, preventing the transmission of required truck related information to the trailer. A possible model-based method to obtain unmeasured states is the Extended Kalman Filter. However, this approach requires elaborate preliminary work steps of high complexity and a sophisticated domain knowledge. Alternatively, this paper proposes the applicability of Neural Networks for estimating the required state and input variables, namely the articulation angle and the truck's steering angle. Two different network types are used: the Feedforward Neural Network and the Nonlinear Autoregressive Exogenous Neural Network. The measured input variables for the networks, in accordance with the inputs of the Extended Kalman Filter in a previous publication, are merely trailer yaw rate and longitudinal speed. In conclusion, a comparison between the results of the Neural Networks and those of the Extended Kalman Filter is drawn.
KW - Extended kalman filter
KW - Feedforward neural network
KW - Nonlinear autoregressive exogenous neural network
KW - State and input estimation
KW - Truck-Semitrailer combination
UR - http://www.scopus.com/inward/record.url?scp=85105047801&partnerID=8YFLogxK
U2 - 10.1016/j.ifacol.2020.12.1372
DO - 10.1016/j.ifacol.2020.12.1372
M3 - Conference article
VL - 53
SP - 14306
EP - 14311
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 2
T2 - 21st IFAC World Congress 2020
Y2 - 12 July 2020 through 17 July 2020
ER -