9 papers
Safe Reinforcement Learning using Ideas from Model Predictive Control
Georg Schäfer, Jakob Rehrl, Stefan Huber +1
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persi…
Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization
Georg Schäfer, Jakob Rehrl, Stefan Huber +1
Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements. A common approach to solving this mul…
Anticipatory Reinforcement Learning for Trajectory Tracking
Georg Schäfer, Jakob Rehrl, Stefan Huber +1
Deep reinforcement learning (DRL) in industrial control often suffers from lag and overshoot due to purely reactive control based on the current tracking error. To achieve anticipa…
Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System
Georg Schäfer, Jakob Rehrl, Stefan Huber
Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its "black-box" exploration risks violating strict hardware safety limi…
Reinforcement Learning for Optimal Experiment Design in Parameter Identification of Mechatronic Systems
Julian Langschwert, Georg Schaefer, Jakob Rehrl +2
Informative excitation signals are critical for accurate system identification of mechatronic systems, yet classical system identification (SI) approaches require expert knowledge…
Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control
Georg Schäfer, Raphael Seliger, Jakob Rehrl +2
Industrial automation increasingly demands energy-efficient control strategies to balance performance with environmental and cost constraints. In this work, we present a multi-obje…