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Formation Tracking of Nonlinear Quadcopters using Robust Model Predictive Control in Unknown Environments | ||
| AUT Journal of Electrical Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 مهر 1404 اصل مقاله (4.28 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2025.23822.5635 | ||
| نویسندگان | ||
| Navid Mohammadi1؛ Saeed Khankalantary* 2؛ Morteza Tayefi1 | ||
| 1Institute of Intelligent Control Systems, K. N. Toosi University of Technology, Tehran, Iran | ||
| 2Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran | ||
| چکیده | ||
| This study presents a robust framework for formation tracking and cooperative control of multiple nonlinear quadcopters, incorporating actuator dynamics, wind drag, and uncertainties. Instead of using torques and forces as control inputs, the theoretical models are developed based on motor speed controls. Designing a controller directly based on actuator signals minimizes uncertainty, improves real-world applicability, and eliminates the need for an additional control allocation algorithm. Two optimal controllers, Linear Quadratic Regulator and Model Predictive Control, are developed and evaluated under dynamic conditions, including uncertainty, disturbances, and obstacle-laden environments. The error-based Predictive Control design minimizes sensitivity to initial conditions and enhances robustness, outperforming Linear Quadratic Regulator in trajectory tracking, energy efficiency, and stability. This framework is extended to a graph-theoretic multi-agent system with four interconnected quadcopters, employing formation control to navigate complex helical paths while maintaining square formations among uncertainties and obstacles. Integrating advanced control methods and graph theory demonstrates coordination and robustness, validating the system's potential for surveillance, rescue missions, and autonomous transfer applications. This research lays the groundwork for extending these methods to larger, heterogeneous agent teams, which can ensure adaptability and precision in real-world scenarios. | ||
| کلیدواژه‌ها | ||
| Formation Tracking؛ Quadcopter؛ Nonlinear Dynamics؛ Multi-Agent Control؛ Model Predictive Control | ||
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آمار تعداد مشاهده مقاله: 103 تعداد دریافت فایل اصل مقاله: 47 |
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