TY - CONF 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 UR - https://opus4.kobv.de/opus4-h-ab/frontdoor/index/index/docId/2007 ER -