Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection

One of the principal requirements for Intelligent Manufacturing Systems that are working in complicated, nonlinear, and uncertain environments is the robust real-time monitoring and an adaptive control system. In this paper, a deep reinforcement learning-based adaptive control framework is proposed that combines multi-sensor fusion, residual-based anomaly detection, and robust state estimation. The framework is tested on a multi-sensor manufacturing dataset that consists of a total of 10,000 time steps of four different heterogeneous sensor streams collected under both normal and anomalous operating conditions. The sensor data is initially preprocessed by noise filtering, normalization, synchronization, and feature extraction, after which an Unscented Kalman Filter (UKF) is used for sensor fusion and state estimation. Residual analysis along with Support Vector Machine (SVM) allows for real-time anomaly detection and classification, while Multi-Model Adaptive Estimation (MMAE) technique improves the robustness of the state estimation. A Proximal Policy Optimization (PPO) agent uses the updated system state and the anomaly information for adaptive control. The results of the experiments reveal perfect anomaly classification with an ROC-AUC of 1.000, highly stable state estimation in all state dimensions, and continuous control performance increase with the average reward of 1.79, thereby validating the proposed framework's suitability for anomaly-aware adaptive control in intelligent manufacturing systems.