A Predefined-Time Robust Sliding Mode Control Based on Zeroing Neural Dynamics for Position and Attitude Tracking of Quadrotor

Sliding mode control (SMC) is considered an efficacious scheme for quadrotor control. However, the control performance of the existing SMC schemes depends on initial states and multiple parameters, and the robustness needs to be improved. To address these issues, a novel predefined-time robust SMC framework based on two zeroing neural dynamics (ZND) schemes, referred to as ZND-based predefined-time robust SMC (ZNDPRSMC) framework, is developed to facilitate position and attitude tracking of a quadrotor under bounded disturbances. Initially, a nonsingular sliding mode surface (SMS) is formulated by incorporating a general ZND along with a differentiable predefined-time activation function. Following this, an approaching law is introduced by utilizing a variable-parameter noise-tolerant ZND and a novel dynamic adaptive parameter. The nonsingular SMS and the approaching law are then combined to construct a nonsingular predefined-time robust controller. The theoretical proofs provided ascertain the predefined-time convergence of the closed-loop system utilizing ZNDPRSMC and its robustness against bounded disturbances. Finally, two trajectory tracking examples of the quadrotor are presented to demonstrate the superiority of the ZNDPRSMC framework.