@article{ZindlerSauerSpielmannetal.2022, author = {Zindler, Klaus and Sauer, Timm and Spielmann, Luca and Gorks, Manuel}, title = {Chancen durch kooperative Logistikflotten}, series = {F{\"o}rdern und Heben}, volume = {2022}, journal = {F{\"o}rdern und Heben}, number = {10}, pages = {36 -- 40}, year = {2022}, abstract = {In Zeiten einer globalen Weltwirtschaft und einem von zunehmendem Wettbewerb gepr{\"a}gten Markt ist die Automatisierung logistischer Prozesse eine Voraussetzung f{\"u}r den Unternehmenserfolg. Vor allem die Steigerung der Produktivit{\"a}t und der Effizienz des innerbetrieblichen Materialflusses nimmt einen hohen Stellenwert ein. Mit dem Einsatz automatisch fahrender Flurf{\"o}rderzeuge (FFZ) l{\"a}sst sich dies erreichen. Als Vision sollten jedoch nicht einzelne automatisch fahrende FFZ gelten. Ziel muss es sein, die Mitglieder einer heterogenen Logistikflotte miteinander zu vernetzen und eine Kommunikations- und Rechenplattform einzurichten. Dies bietet ein großes Potenzial im Hinblick auf die Auftragsplanung zur Verbesserung der Wirtschaftlichkeit sowie in den Bereichen Lokalisierung und Sicherheit zur Optimierung automatisch fahrender FFZ.}, subject = {Flurf{\"o}rderer}, language = {de} } @inproceedings{SauerSpielmannGorksetal.2023, author = {Sauer, Timm and Spielmann, Luca and Gorks, Manuel and Zindler, Klaus and Jumar, Ulrich}, title = {Model Predictive Control of Industrial Trucks with AI-based Plant Model Selection}, series = {9th International Conference on Control, Decision and Information Technologies (CoDIT)}, volume = {2023}, booktitle = {9th International Conference on Control, Decision and Information Technologies (CoDIT)}, number = {Conference Proceedings}, doi = {10.1109/CoDIT58514.2023.10284427}, pages = {263 -- 268}, year = {2023}, abstract = {In this paper, a new control concept for automatic track guidance of forklifts within a heterogeneous logistics fleet is presented. The proposed control scheme is universally applicable and based on Artificial Intelligence (AI). The lateral control is realized using a Model Predictive Controller (MPC). In order to take into account the diversity of the industrial truck variants, a multi-model approach is used. Therefore, a plant model for each truck variant is integrated into the MPC. In a practical application, the most suitable model has to be selected. This decision is based on AI in the form of an Artificial Neural Network (ANN). It is able to distinguish the different truck variants based on that part of the plant's state vector, which represents the vehicle dynamic characteristics. Thus, the appropriate model can be used, which significantly improves the control quality and guarantees an accurate track guidance of different forklifts. Due to the AI-based selection of the model, the computational effort can be kept low and real-time capability can be ensured. In order to classify the performance of the proposed control concept, its simulation results are compared to the closed-loop behavior, using a classical MPC.}, subject = {Flurf{\"o}rderer}, language = {en} } @inproceedings{SauerGorksSpielmannetal.2023, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Hepp, Nils and Zindler, Klaus and Jumar, Ulrich}, title = {AI-based control approaches for lateral vehicle guidance of industrial trucks}, series = {IFAC WC 2023 - The 22nd World Congress of the International Federation of Automatic Control 2023, Yokohama, Japan, 09.07.2023 - 14.07.2023}, booktitle = {IFAC WC 2023 - The 22nd World Congress of the International Federation of Automatic Control 2023, Yokohama, Japan, 09.07.2023 - 14.07.2023}, doi = {https://doi.org/10.1016/j.ifacol.2023.10.1501.}, year = {2023}, abstract = {Two different control concepts for the automatic track guidance of forklifts are proposed. Both approaches are based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI), and are able to take into account time-variant parameters, such as the vehicle velocity, and to reduce the influence of the path curvature, the most important disturbance variable of lateral vehicle control. In the first approach, both, the path curvature and the vehicle velocity signal, are provided to the controller in addition to the state variables of the controlled system. By varying the corresponding parameters in the training process, both signals can be considered and the control parameters can be optimized accordingly. In the second approach, several controllers (multi-model concept) considering the path curvature are used and the varying vehicle velocity is taken into account using a gain-scheduling concept. Considering time-variant vehicle parameters and the influence of the disturbance variable during operation, a stable track guidance is guaranteed within the whole speed range of the industrial trucks.}, subject = {Flurf{\"o}rderer}, language = {en} }