H Time-Delayed Fractional Order Adaptive Sliding Mode Control for Two-Wheel Self-Balancing Vehicles

Area of Science:

• Robotics and Control Systems
• Nonlinear Control Theory
• Mechatronics

Background:

• Two-wheel self-balancing vehicles (TWSBVs) present significant control challenges due to their inherent instability.
• Traditional control methods often struggle with uncertainties and external disturbances common in real-world applications.
• Fractional-order calculus offers enhanced flexibility in modeling and control design for complex dynamic systems.

Purpose of the Study:

• To propose a robust and adaptive control algorithm for TWSBVs.
• To address challenges posed by time delays, uncertainties, and external disturbances in TWSBV systems.
• To validate the proposed control algorithm through experimental implementation.

Main Methods:

• Development of a time-delayed fractional-order adaptive sliding mode control (TD-FOASMC) algorithm.
• Utilizing Lyapunov-Razumikhin functions for closed-loop stability analysis.
• Designing a robust switching function to handle state-dependent uncertainties without prior bounds.
• Experimental validation using a TWSBV equipped with MPU-6050 (gyroscope/accelerometer), TB6612FNG motor driver, and STM32F103x8B microcontroller.

Main Results:

• The proposed TD-FOASMC algorithm successfully enabled autonomous balancing of the TWSBV.
• The system demonstrated rapid recovery to a stable state following the introduction of disturbances.
• The control strategy proved effective in managing uncertainties without requiring prior knowledge of their bounds.
• The experimental setup confirmed the practical feasibility and performance of the proposed control method.

Conclusions:

• The time-delayed fractional-order adaptive sliding mode control algorithm is a highly effective solution for stabilizing two-wheel self-balancing vehicles.
• This approach offers significant robustness against system uncertainties and external disturbances.
• The experimental results validate the algorithm's capability for autonomous balancing and rapid stabilization.