2024/06/28 by Tobias Nagel, Nagel, Tobias, Marco F. Huber +1 · 1 citation
Computer Science · Engineering · #Evolutionary Algorithms and Applications #FOS: Electrical engineering #Reinforcement Learning in Robotics #Systems and Control (eess.SY) #Traffic control and management #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.19817
openalex publication_date 2024/06/28 · openalex created_date 2024/07/02 · openalex updated_date 2026/07/28
The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system's governing equations, especially in complex environments that combine physical laws of different disciplines. In this paper, we present a new approach that allows identifying an ordinary differential equation by means of a physics-informed machine learning algorithm. Our method introduces a special neural network that allows exploiting prior human knowledge to a certain degree and extends it autonomously, so that the resulting differential equations describe the system as accurately as possible. We validate the method on a Duffing oscillator with simulation data and, additionally, on a cascaded tank example with real-world data. Subsequently, we use the developed algorithm in a model-based reinforcement learning framework by alternately identifying and controlling a system to a target state. We test the performance by swinging-up an inverted pendulum on a cart.