Application of Digital Twin Technology for Predictive Control andLifecycle Optimization in Autonomous Industrial Robotics
Keywords:
Digital Twin, Predictive Control, Industrial Robotics, Lifecycle Optimization, Autonomous Systems, Industry 4.0, Smart ManufacturingAbstract
This paper explores the integration of Digital Twin (DT) technology into autonomous industrial robotics with a focus on predictive control and lifecycle optimization. As smart factories evolve with Industry 4.0, DTs provide a real-time digital representation of physical systems, enabling enhanced predictive analytics, maintenance, and operational decision-making. We analyze the role of DT in minimizing unplanned downtime, improving task execution, and extending the asset lifecycle of autonomous robotic systems. Drawing on multiple studies, the paper identifies key methods, challenges, and outcomes related to DT deployment in robotic control environments. A literature review reveals that DT-driven robotics significantly improve lifecycle metrics, while predictive control algorithms, when integrated into DT platforms, offer up to 40% improvement in fault response and resource utilization. Two tables summarize technical implementations and performance comparisons from key studies
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Copyright (c) 2025 Venkatesh Murthy, (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
