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Dynamic Safety-Augmented μ-Synthesis for Robust Control of Uncertain Robotic Manipulators | ||
| International Journal of Industrial Electronics Control and Optimization | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 مهر 1405 اصل مقاله (1.39 M) | ||
| نوع مقاله: Research Articles | ||
| شناسه دیجیتال (DOI): 10.22111/ieco.2026.55037.1753 | ||
| نویسنده | ||
| Farnaz Sabahi* | ||
| Electrical Engineering Department, Electrical and Computer Engineering Faculty, Urmia University, Urmia, Iran. | ||
| چکیده | ||
| Robotic manipulators operating in safety-critical environments must simultaneously guarantee accurate trajectory tracking, disturbance rejection, and state safety in the presence of model uncertainties. Conventional approaches typically combine μ-synthesis with Control Barrier Functions (CBFs) in a cascaded architecture, where an online safety filter is applied after controller synthesis. However, this sequential design introduces unnecessary conservatism and precludes a unified theoretical treatment of robustness and safety. To address this limitation, this paper proposes a Dynamic Safety-Augmented μ-Synthesis (DSA-μ) framework that embeds safety dynamics directly into the generalized plant prior to controller synthesis. Parametric uncertainties are modeled using Linear Fractional Transformations (LFTs), while a dynamic safety state converts barrier conditions into augmented performance outputs for D–K iteration. The resulting integrated synthesis procedure yields a controller that simultaneously guarantees robust stability, disturbance attenuation, and forward invariance of the safe set. Numerical studies on a 3-DOF planar manipulator demonstrate that the proposed DSA-μ framework reduces trajectory-tracking RMSE by 35.5% compared with a conventional cascaded μ–CBF architecture while maintaining a positive safety margin throughout the simulations under 20% parametric model uncertainty. | ||
| کلیدواژهها | ||
| μ-synthesis؛ Control Barrier Function (CBF)؛ safety-critical robotics؛ structured uncertainty؛ robotic manipulators | ||
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