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 -