TY - JOUR A1 - Zindler, Klaus A1 - Sauer, Timm A1 - Spielmann, Luca A1 - Gorks, Manuel T1 - Chancen durch kooperative Logistikflotten JF - Fördern und Heben N2 - In Zeiten einer globalen Weltwirtschaft und einem von zunehmendem Wettbewerb geprägten Markt ist die Automatisierung logistischer Prozesse eine Voraussetzung für den Unternehmenserfolg. Vor allem die Steigerung der Produktivität und der Effizienz des innerbetrieblichen Materialflusses nimmt einen hohen Stellenwert ein. Mit dem Einsatz automatisch fahrender Flurförderzeuge (FFZ) lässt 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. KW - Flurförderer KW - Autonomes Fahrzeug Y1 - 2022 VL - 2022 IS - 10 SP - 36 EP - 40 ER - TY - CHAP A1 - Sauer, Timm A1 - Spielmann, Luca A1 - Gorks, Manuel A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Model Predictive Control of Industrial Trucks with AI-based Plant Model Selection T2 - 9th International Conference on Control, Decision and Information Technologies (CoDIT) N2 - 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. KW - automatic control KW - robust control KW - AI KW - ANN KW - Flurförderer KW - Künstliche Intelligenz Y1 - 2023 UR - https://ieeexplore.ieee.org/abstract/document/10284427 U6 - https://doi.org/10.1109/CoDIT58514.2023.10284427 VL - 2023 IS - Conference Proceedings SP - 263 EP - 268 ER - TY - CHAP A1 - Sauer, Timm A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Hepp, Nils A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - AI-based control approaches for lateral vehicle guidance of industrial trucks T2 - IFAC WC 2023 – The 22nd World Congress of the International Federation of Automatic Control 2023, Yokohama, Japan, 09.07.2023 - 14.07.2023 N2 - 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. KW - Artificial intelligence, Lateral vehicle control, Intelligent transportation systems KW - Flurförderer KW - Künstliche Intelligenz Y1 - 2023 UR - https://www.sciencedirect.com/science/article/pii/S2405896323019092 U6 - https://doi.org/https://doi.org/10.1016/j.ifacol.2023.10.1501. ER -