Optimal PID control for hovering stabilization of quadcopter using long short term memory. (August 2022)

• Record Type: Journal Article
• Authors: Yoon, Jaehyun; Doh, Jaehyeok
• Is Part Of: Advanced engineering informatics. Volume 53(2022)
• Publication Date: 2022-08
• Subjects: Quadcopter -- PID control -- Neural networks (NN) -- Long short term memory (LSTM) -- Non-dominated sorting genetic algorithm-II (NSGA-II); Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101679
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Drones are a type of unmanned aerial vehicle. They use several rotors to control their flight motion and stabilize their attitude. This study aims to determine the optimal proportional-integral–differential (PID) gain values that can stabilize a quadcopter with four rotors quickly when its attitude is disturbed. Generally, expert knowledge and a great deal of time and money are required to obtain the PID gain values to stabilize the attitude of a drone. However, in this study, long short-term memory (LSTM), which is a type of neural network algorithm, was used to evaluate the flight motions based on PID gain values without expert knowledge of quadcopters (quadcopter flight motion, PID control algorithms, and quadcopter expert experience). To obtain the optimal values of the PID gain for stabilizing the attitude of a drone, a PID simulator algorithm was developed in this study. The developed algorithm used dynamic equations of motion for the drones. Simulations were used to acquire the drone stabilization data, and a back-propagation neural network was applied to establish an approximate model. Subsequently, the non-dominated sorting genetic algorithm-II was used to obtain the optimal PID gain values that could restore the attitude of the drone quickly. The drone motion data obtained using the simulations were used as the LSTM training data, and the optimal PID gain values obtained using the genetic algorithm were used by the LSTM to predict the motion. To verify the results, a drone was constructed, and the LSTM and dynamic simulation values were compared with the drone experimental values using an experimental device that allowed the motion of the drone to be examined. The results indicated that the motions resulting from the optimal values and the experimental results were in agreement.