Predefined-time sliding mode control for position and attitude of quadrotor with variable exponent coefficients

Area of Science:

• Robotics and Control Systems
• Nonlinear Dynamics
• Artificial Intelligence in Control

Background:

• Quadrotor systems require robust control for stability and trajectory tracking.
• Existing control methods often struggle with systems exhibiting variable dynamics or achieving rapid convergence.
• Sliding mode control (SMC) offers robustness but can face chattering and convergence time issues.

Purpose of the Study:

• To develop novel predefined-time sliding mode control (SMC) algorithms for quadrotor systems.
• To address the challenge of variable exponent coefficients in quadrotor dynamics.
• To ensure system stability and achieve predefined-time convergence to a desired target.

Main Methods:

• Development of a novel predefined-time sliding mode control algorithm.
• Integration of predefined-time neural networks into the sliding mode control framework (PTNN-SMC).
• Stability analysis using Lyapunov methods to guarantee predefined-time convergence.
• Extensive numerical simulations on diverse nonlinear systems.

Main Results:

• The proposed PT-SMC and PTNN-SMC algorithms successfully achieve predefined-time convergence for quadrotor systems.
• Demonstrated adaptability to systems with variable exponent coefficients.
• Stability analysis confirmed the theoretical capabilities of the developed controllers.
• Numerical simulations showed superior performance compared to conventional SMC and neural network control.

Conclusions:

• The novel predefined-time control strategies offer significant improvements in convergence speed and stability for quadrotor systems.
• The developed algorithms are effective in handling nonlinear dynamics with variable coefficients.
• This research provides a robust framework for advanced quadrotor control applications.