文献类型：期刊文献

英文题名：Iterative Learning Based Cascade Sliding Mode Control for Quadrotor UAV Trajectory Tracking

作者：Qian, Shiyu[1]; Xu, Jing[1]; Niu, Yugang[1]

机构：[1] The Key Laboratory of Smart Manufacturing in Energy Chemical Process, East China University of Science and Technology, Ministry of Education, Shanghai, 200237, China

年份：2024

起止页码：13

外文期刊名：Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024

收录：EI(收录号：20250817905217)

语种：英文

外文关键词：Aircraft detection - Cascade control systems - Closed loop control systems - Proportional control systems - Two term control systems

摘要：The inherent under-actuation and strong coupling properties of quadrotor introduce unique complexities, which leads that conventional control methods often struggle with low control precision and limited anti-disturbance capability. Since attitude loop control plays a crucial role in the trajectory tracking of quadrotor, this work proposes an innovative cascade proportional-sliding mode controller (P-SMC) enhanced with iterative learning control (ILC) specifically for high-performance attitude loop control. The P-SMC effectively manages the quadrotor's attitudes and angular velocity, suppressing external disturbances and uncertainties. Meanwhile, ILC with forgetting factor compensates for repeatable dynamics, further enhancing tracking accuracy. By leveraging learning from previous iterations, this co-design approach ensures robust inner-loop performance. Experimental results validate the superiority of this approach compared to standalone PID control, SMC methods and super-twisting SMC, demonstrating its effectiveness in enhancing quadrotor trajectory tracking performance, particularly in the critical attitude loop control aspect. Thus, the co-design of ILC with SMC was effective to enhance the control performance, and the assistant of ILC was useful to avoid the chattering effect of SMC and improve the high frequency oscillator of control signal. ? 2024 IEEE.