Reinforcement learning-based adaptive control strategies for sustainable production systems

Abstract

Abstract. The need to achieve sustainable production has become an urgent necessity in the conditions of stricter environmental requirements and the rise in the cost of energy worldwide. The classical proportionalintegralderivative controllers and linear Model Predictive Controllers are conventional model-based control strategies that by nature rely on precise process models and fixed optimization horizons and hence are not well suited to the dynamic, non-linear, and complex nature of the modern production environment. The current paper suggests a new adaptive control system based on Reinforcement Learning (RL), where the overall production system is optimized and controlled to achieve Sustainable Production Systems, with the multi-objective rewarding function, which aims to minimize the Overall Equipment Effectiveness (OEE), defect rate, specific energy consumption, and carbon dioxide emissions, and a physics-informed Digital Twin safety filter that stops unsafe policy execution in training and deployment. The proposed framework is evaluated on a multi-machine flexible manufacturing cell benchmark, which includes CNC milling, turning, and robotic assembly, and yields an OEE of 93.6, a defect rate of 0.9, a decrease in specific energy consumption of 27.8, and a decrease in CO 2 emissions of 23.4 compared to PID baseline controllers. Experiments with ablation prove all the above-mentioned in the necessity of each of the architectural constituents, and the Pareto frontier analysis proves that the proposed SAC agent is the best in the OEE-versus-energy trade-off space compared to all of the competing methods.