TY - GEN
T1 - Parallel Control System PD-SMCNN for Robust Autonomous Mini-Quadcopter
AU - Wahyuadnyana, Kadek Dwi
AU - Darwito, Purwadi Agus
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - A parallel control system with proportional-derivative (PD) and sliding mode control-neural network (SMCNN) has been designed to control the position and attitude of mini-quadcopter parrot mambo mini-drone (PMD). This PDSMCNN control scheme is implemented in the PMD system through a 3D environment using MATLAB-Simulink, which represented the real conditions. The PD controller is used to reach the minimum value of the gain for the PMD getting to take-off, while the SMC as a robust controller is adjusted using a backpropagation neural network (NN) to make the PMD more robust regarding an external disturbance. The software and hardware simulation have been conducted to validate the proposed controller with a mission plan as an input. Based on the simulation results, the PD controller shows an overshoot of 26.7% then becomes 0% of overshoot when the PD-SMCNN controller is implemented. From the hardware simulation results, it is found that the PD controller has 45.1% of robustness and the PD-SMCNN controller has 100% of robustness in the absence of external disturbance. Next, it is found that the PD controller has 26.9% of robustness and the PD-SMCNN has 74.8% of robustness in the presence of external disturbance. Based on these results, indicates that the proposed PD-SMCNN controller is superior to the PD controller in terms of robustness.
AB - A parallel control system with proportional-derivative (PD) and sliding mode control-neural network (SMCNN) has been designed to control the position and attitude of mini-quadcopter parrot mambo mini-drone (PMD). This PDSMCNN control scheme is implemented in the PMD system through a 3D environment using MATLAB-Simulink, which represented the real conditions. The PD controller is used to reach the minimum value of the gain for the PMD getting to take-off, while the SMC as a robust controller is adjusted using a backpropagation neural network (NN) to make the PMD more robust regarding an external disturbance. The software and hardware simulation have been conducted to validate the proposed controller with a mission plan as an input. Based on the simulation results, the PD controller shows an overshoot of 26.7% then becomes 0% of overshoot when the PD-SMCNN controller is implemented. From the hardware simulation results, it is found that the PD controller has 45.1% of robustness and the PD-SMCNN controller has 100% of robustness in the absence of external disturbance. Next, it is found that the PD controller has 26.9% of robustness and the PD-SMCNN has 74.8% of robustness in the presence of external disturbance. Based on these results, indicates that the proposed PD-SMCNN controller is superior to the PD controller in terms of robustness.
KW - Autonomous quadcopter
KW - PD-SMCNN
KW - neural network
KW - parrot mambo mini-drone
KW - robust controller
KW - sliding mode control
UR - http://www.scopus.com/inward/record.url?scp=85137873986&partnerID=8YFLogxK
U2 - 10.1109/ISITIA56226.2022.9855371
DO - 10.1109/ISITIA56226.2022.9855371
M3 - Conference contribution
AN - SCOPUS:85137873986
T3 - 2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding
SP - 244
EP - 249
BT - 2022 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022
Y2 - 20 July 2022 through 21 July 2022
ER -