Scopus

Model Order Reduction for Multi-Agent Robotic Arm Systems Using Positive-Real and Balanced Truncation Methods

Năm XB 2026 Tạp chí / Hội thảo Advances in Information and Communication Technology: Proceedings of the 4th International Conference, ICTA 2025, Volume 2 Volume 2, pp. 126-137 Đơn vị ICTU DOI / Link https://link.springer.com/chapter/10.1007/978-3-032-18159-6_14 ↗

Tác giả

Von-Dim Nguyen

Tóm tắt

The rapid growth in the scale and complexity of robotic arm systems, whether deployed individually or in multi-agent networks, creates significant obstacles for real-time control, simulation, and practical implementation. This work proposes an integrated model order reduction framework that unifies positive-real balanced truncation for single-agent robots with balanced truncation and clustering strategies for large-scale multi-agent configurations. Using a benchmark four-degree-of-freedom manipulator and a cyclic network of six agents, all methods are systematically implemented and evaluated in MATLAB. Reduced-order models are constructed for a range of target orders, and their fidelity is assessed through H₂ and H∞ error norms, as well as time- and frequency-domain analyses. Results demonstrate that, for individual robotic arms, reducing the model order to four preserves key dynamic behaviors with minimal loss (H₂, H∞ errors below 0.01), while further reduction increases the error but may remain acceptable for less demanding applications. For multi-agent networks, model reduction from order 48 to 16 incurs virtually no loss of accuracy, and even highly compact models (orders 2-4) closely approximate the full system in most scenarios. The approach achieves significant simplification without compromising stability or passivity, making it well-suited for efficient controller synthesis and real-time deployment. These findings offer practical, evidence-based guidance for engineers and researchers seeking scalable and reliable model reduction in advanced robotic systems.

Tài liệu tham khảo

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Ghi chú

Source ID: 249; Author classification: ICTU - Main Author; First listed author: Von-Dim Nguyen; Contact author: Thanh-Tung Nguyen.; Total authors: 5; Springer matched from provided ICTA 2025 volumes

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