Dual Closed-Loop Algorithm Based on Adaptive Single-Neuron PID Fusion with Self-Tuning Sliding Mode Control

Aiming at the unstable flight attitude control of quadcopter UAV nowadays, and there is a delay phenomenon in the control, this paper proposes a dual-loop control method to design the control rate, and designs a single neuron adaptive PID algorithm to be used as the inner-loop algorithm for horizontal attitude control, and uses adaptive sliding-mode control to be used as the outer-loop algorithm for vertical cover, and the single neuron adaptive PID algorithm makes up for the artificial parameter adjustment. The single neuron adaptive PID algorithm compensates for the disadvantages of manual parameter tuning, improves the corresponding speed and reduces the amount of overshooting. The adaptive sliding mode control is optimized for the ground effect and vibration, and after simulation tests comparing the inner and outer pd control algorithms, it can be concluded that the dual closed-loop algorithm based on adaptive single neuron PID fused with self-tuning sliding mode control is more stable, more responsive, with shorter time delay and better robustness.