TY - CHAP A1 - Sauer, Timm A1 - Zindler, Klaus A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Jumar, Ulrich T1 - Automatic track guidance of industrial trucks using self-learning controllers considering a priori plant knowledge T2 - IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France N2 - This paper presents a new self-learning control scheme for lateral track guidance of industrial trucks using artificial intelligence. It is an universally applicable lateral dynamic control concept which is able to adapt itself to different truck variants. Moreover it shall consider vehicle parameter variations that occur during operation, such as the load dependent change of vehicle mass and moment of inertia. The proposed approach uses Reinforcement Learning. In order to reduce the training effort, a new concept is realized, taking into account a priori knowledge of vehicle behavior. Its fundamental idea consists of dividing the training process into two steps. In the first step the controller will be pre-trained on basis of a nominal model representing a priori knowledge of lateral dynamic vehicle behavior. Since this model is derived for an industrial truck with average vehicle parameter values, a fine tuning of the control parameters has to be performed in the second step. In this way the controller is adapted to the actual truck variant and the corresponding vehicle parameter values. In order to demonstrate the efficiency of the proposed control scheme, the simulation results given in this paper are compared to the closed loop behavior using standard LQR. KW - Labor für Simulation, Steuerung und Regelung KW - Kooperative Autonome Intralogistik Systeme KW - Flurförderer KW - Spurführung KW - Künstliche Intelligenz Y1 - 2021 ER - TY - JOUR A1 - Sauer, Timm A1 - Gorks, Manuel A1 - Spielmann, Luca A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - Adaptive self-learning controllers with disturbance compensation for automatic track guidance of industrial trucks JF - SICE Journal of Control, Measurement and System Integration N2 - This paper presents an extended control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a simplified linear model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step using a more complex nonlinear model, representing the real industrial truck. In this way, the controller is adapted to the actual truck variant and the corresponding parameter values. By using the nonlinear model, it can be ensured that the forklift’s dynamic is approximated within the entire operating range, even at high steering angles. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significantly improves the control quality. In order to demonstrate this, the new approach is compared to an RL control concept, which is not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach. KW - Flurförderer KW - Spurführung KW - Künstliche Intelligenz Y1 - 2023 U6 - https://doi.org/10.1080/18824889.2023.2183009 VL - 2023 IS - Vol. 16, No. 1 SP - 84 EP - 97 ER - TY - JOUR A1 - Sauer, Timm A1 - Zindler, Klaus A1 - Jumar, Ulrich T1 - KI-basierte Regelungskonzepte zur automatischen Spurregelung von Flurförderzeugen JF - at - Automatisierungstechnik N2 - In diesem Beitrag werden zwei KI-basierte Regelungskonzepte zur automatischen Spurführung von Flurförderzeugen (FFZ) einer heterogenen Logistikflotte vorgestellt. Während die modellfreien Verfahren des bestärkenden Lernens für diese Anwendung als Vertreter der direkten neuronalen Regelung vorgestellt werden, lässt sich das zweite Regelungskonzept in die Klasse der indirekten neuronalen Regelungen einordnen. Beide Konzepte zeichnen sich dadurch aus, dass sie dazu in der Lage sind, die Reglerparameter an verschiedene Varianten von FFZ anzupassen sowie vorab bekanntes Wissen bezüglich des Regelstreckenverhaltens beim Entwurf der Regler gezielt zu berücksichtigen. KW - Automatische Spurführung; Künstliche Intelligenz KW - Flurförderer KW - Künstliche Intelligenz KW - Spurführung Y1 - 2024 UR - https://www.degruyter.com/document/doi/10.1515/auto-2023-0155/html U6 - https://doi.org/https://doi.org/10.1515/auto-2023-0155 VL - 72 IS - 4 SP - 336 EP - 353 ER -