Iterative Learning Based Cascade Sliding Mode Control for Quadrotor UAV Trajectory Tracking

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.