Neuroadaptive dynamic surface control for induction motors stochastic system based on reduced-order observer.

Area of Science:

• Electrical Engineering
• Control Systems
• Artificial Intelligence

Background:

• Induction motors are crucial in industrial applications, but their performance can degrade due to stochastic disturbances.
• Traditional control methods like backstepping control face computational complexity challenges.

Purpose of the Study:

• To develop an observer-based neural network control scheme for induction motor position tracking.
• To mitigate the effects of stochastic disturbances on the motor system.
• To address the computational complexity associated with traditional control techniques.

Main Methods:

• A reduced-order observer is designed to estimate the angular velocity.
• Neural networks are employed to approximate nonlinear functions within the system.
• Stochastic Lyapunov functions are utilized for stability analysis.
• Dynamic surface control techniques are integrated to simplify computations.

Main Results:

• The proposed control scheme effectively reduces the impact of stochastic disturbances.
• The system demonstrates faster tracking speeds and smaller tracking errors compared to traditional methods.
• The designed observer accurately estimates critical system signals.
• The dynamic surface control approach alleviates computational burdens.

Conclusions:

• The observer-based neural network control scheme offers a robust and efficient solution for induction motor position tracking.
• This approach enhances system performance by minimizing disturbances and improving accuracy.
• The integration of neural networks and dynamic surface control presents a significant advancement in motor control strategies.