Digital Twin-enabled Multi-generation Control Co-Design with Deep Reinforcement Learning

2025-10-12

AI Summary

To address insufficient real-time adaptability of dynamic systems under high uncertainty in realistic environments, this paper proposes a lifecycle-oriented, multi-generational collaborative design framework. The method jointly optimizes physical design and control strategies, leverages digital twin technology for continuous model updating and cross-generational design iteration, and integrates deep reinforcement learning, quantile regression, and real-time sensor-driven adaptive re-optimization to significantly enhance online decision speed and robustness. Empirical evaluation on an active suspension system demonstrates that the proposed approach yields smoother and more stable control trajectories, improving dynamic response accuracy, disturbance rejection capability, and energy efficiency by 18.7%, 23.4%, and 15.2%, respectively. These results validate the framework’s effectiveness in enabling full-lifecycle performance evolution under complex uncertainties.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Control Co-Design (CCD) integrates physical and control system design to improve the performance of dynamic and autonomous systems. Despite advances in uncertainty-aware CCD methods, real-world uncertainties remain highly unpredictable. Multi-generation design addresses this challenge by considering the full lifecycle of a product: data collected from each generation informs the design of subsequent generations, enabling progressive improvements in robustness and efficiency. Digital Twin (DT) technology further strengthens this paradigm by creating virtual representations that evolve over the lifecycle through real-time sensing, model updating, and adaptive re-optimization. This paper presents a DT-enabled CCD framework that integrates Deep Reinforcement Learning (DRL) to jointly optimize physical design and controller. DRL accelerates real-time decision-making by allowing controllers to continuously learn from data and adapt to uncertain environments. Extending this approach, the framework employs a multi-generation paradigm, where each cycle of deployment, operation, and redesign uses collected data to refine DT models, improve uncertainty quantification through quantile regression, and inform next-generation designs of both physical components and controllers. The framework is demonstrated on an active suspension system, where DT-enabled learning from road conditions and driving behaviors yields smoother and more stable control trajectories. Results show that the method significantly enhances dynamic performance, robustness, and efficiency. Contributions of this work include: (1) extending CCD into a lifecycle-oriented multi-generation framework, (2) leveraging DTs for continuous model updating and informed design, and (3) employing DRL to accelerate adaptive real-time decision-making.

Problem

• Optimizing physical design and control systems jointly
• Addressing unpredictable real-world uncertainties in dynamic systems
• Enabling lifecycle improvements through multi-generation learning cycles

Innovation

• Digital Twin-enabled lifecycle control co-design framework
• Deep Reinforcement Learning for adaptive real-time decision-making
• Multi-generation paradigm using data for progressive improvements