Adaptive fuzzy neural super-twisting control of micro gyroscope sensor

Area of Science:

• Control Systems Engineering
• Robotics
• Mechatronics

Background:

• Micro-gyroscopes require precise vibration control for accurate measurements.
• Traditional sliding mode control suffers from system chattering.
• Nonlinear system uncertainties pose challenges in gyroscope control.

Purpose of the Study:

• To design an adaptive super-twisting sliding mode control (STSMC) for micro-gyroscopes.
• To enhance system stability and minimize tracking errors using a two-loop recursive fuzzy neural network (TLRFNN).
• To address online parameter estimation and system uncertainties.

Main Methods:

• Utilizing an adaptive technology based on parameter linearization for online estimation of unknown nonlinear system parameters.
• Implementing STSMC to overcome chattering issues inherent in conventional sliding mode control.
• Employing a two-loop recursive fuzzy neural network (TLRFNN) for approximating system uncertainties and storing prior information.
• Deriving adaptive laws within the Lyapunov stability framework to ensure system stability.

Main Results:

• The proposed STSMC system demonstrates good tracking performance in micro-gyroscopes.
• The system effectively maintains vibrations of the gyroscope proof mass.
• Achieved root mean square errors (RMSE) of [Formula: see text] and [Formula: see text] in the x and y directions, respectively, indicating minimal tracking error.

Conclusions:

• The adaptive STSMC based on TLRFNN provides a robust and stable control solution for micro-gyroscopes.
• The method successfully mitigates chattering and handles system uncertainties effectively.
• The simulation results validate the proposed control system's capability for precise vibration maintenance and accurate tracking.