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Automatic track guidance of industrial trucks using AI-based controllers with disturbance compensation

  • This paper presents a new 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 model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. 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 significanlty improves the control quality. In order to demonstrate this, the new approach is compared to a RL control concept, not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.

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Metadaten
Author:Timm Sauer, Luca Spielmann, Manuel Gorks, Klaus Zindler, Ulrich Jumar
Parent Title (German):61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan
Document Type:Conference Proceeding
Language:English
Year of Completion:2022
Release Date:2022/10/25
Tag:Artificial Intelligence; Automatic Control; Intelligent Transportation Systems
GND Keyword:Künstliche Intelligenz; Flurförderer
Urheberrecht:1
Institutes:Fakultät Ingenieurwissenschaften / Labore / Simulation, Steuerung und Regelung
research focus :Intelligent Systems / Intelligent Mobility
Licence (German):Keine Lizenz - es gilt das deutsche Urheberrecht
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