TY - GEN
T1 - Control System on Multi-Quadcopter Based Sliding Mode Control (SMC) - Extended Kalman Filter (EKF) to Generate Optimal Trajectory Tracking Stability
AU - Agustina, Nilla Perdana
AU - Darwito, Purwadi Agus
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The trajectory tracking control system created will be used to support the development of quadcopter formation strategies. To form a multi-quadcopter formation in the hovering phase, a stable and robust control system is required. The follower estimation in leader-follower formation strategy is implemented with Extended Kalman Filter. Sliding Mode Control is a robust control system strategy used to control the multi-quadcopter thoroughly. Trajectory results have been achieved, but the attitude of the multi-quadcopter during the flight tracking trajectory is unstable. Therefore, the addition of optimization can be used to generate trajectories corresponding to optimal stability. Trajectory optimization by the follower is achieved more optimally with the addition of Particle Swarm Optimization, the altitude value is achieved 100% and the follower's RSME on pitch averages 0.05 and yaw 0.003.
AB - The trajectory tracking control system created will be used to support the development of quadcopter formation strategies. To form a multi-quadcopter formation in the hovering phase, a stable and robust control system is required. The follower estimation in leader-follower formation strategy is implemented with Extended Kalman Filter. Sliding Mode Control is a robust control system strategy used to control the multi-quadcopter thoroughly. Trajectory results have been achieved, but the attitude of the multi-quadcopter during the flight tracking trajectory is unstable. Therefore, the addition of optimization can be used to generate trajectories corresponding to optimal stability. Trajectory optimization by the follower is achieved more optimally with the addition of Particle Swarm Optimization, the altitude value is achieved 100% and the follower's RSME on pitch averages 0.05 and yaw 0.003.
KW - extended kalman filter
KW - leader-follower
KW - multiquadcopter
KW - sliding mode control
KW - trajectory tracking
UR - http://www.scopus.com/inward/record.url?scp=85198834857&partnerID=8YFLogxK
U2 - 10.1109/SIML61815.2024.10578288
DO - 10.1109/SIML61815.2024.10578288
M3 - Conference contribution
AN - SCOPUS:85198834857
T3 - 2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024
SP - 19
EP - 24
BT - 2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024
Y2 - 6 June 2024 through 7 June 2024
ER -