Oktaf Agni Dhewa, Fatchul Arifin, Ardy Seto Priambodo, Anggun Winursito, Yasir Mohd Mustafah
The stability of an Unmanned Aerial Vehicle (UAV) attitude is crucial in aviation to mitigate the risk of accidents and ensure mission success. This study aims to optimize and adaptively control the flight attitude stability of a flying wing-type UAV amidst environmental variations. This is achieved through the utilization of Linear Quadratic Regulator-Neural Network (LQR-NN) control, wherein the Neural Network predicts the optimal K gain value by fine-tuning Q and R parameters to minimize system errors. An online learning neural network adjusts the K value based on real-time error feedback, enhancing system performance. Experimental results demonstrate improved stability metrics: for roll angle stability, a rise time of 0.4682 seconds, settling time of 1.3819 seconds, overshoot of 0.298%, and Steady State Error (SSE) of 0.133 degrees; for pitch angle stability, a rise time of 0.2309 seconds, settling time of 0.7091 seconds, overshoot of 0.1224%, and Steady State Error (SSE) of 0.0239 degrees. The LQR-NN approach effectively reduces overshoot compared to traditional Linear Quadratic Regulator (LQR) control, thereby minimizing oscillations. Furthermore, LQR-NN can minimize the Steady State Error (SSE) to 0.074 degrees for roll rotation motion and 0.035 degrees for pitch rotation motion. © (2024), (International Islamic University Malaysia-IIUM). All rights reserved.
Dept. of Electrical and Electronic Engineering, Vocational Faculty, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; Dept. of Electronics and Informatics Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; Dept. of Mechatronics Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, Selangor, Malaysia
