Robust adaptive control based on RBF neural network for stochastic electromagnetic suspension system.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Electromagnetic suspension systems exhibit inherent instability due to nonlinear dynamics and sensitivity to disturbances.
• Designing controllers for robust stability and high performance in these systems is a significant challenge.
• Model uncertainty and stochastic noise further complicate controller design.

Purpose of the Study:

• To propose a robust adaptive neural network controller for electromagnetic suspension systems.
• To address challenges posed by model uncertainty and stochastic disturbances.
• To achieve robust stability and high performance in electromagnetic suspension systems.

Main Methods:

• Utilized a radial basis function neural network for approximating unknown system parameters.
• Employed the stochastic bounded stability theorem to manage stochastic noise.
• Integrated command filter technique with minimal learning parameter method to simplify design and reduce computational load.

Main Results:

• The proposed controller effectively mitigated the impact of stochastic disturbances.
• Demonstrated robust stability in the presence of model uncertainties.
• Achieved high performance in electromagnetic suspension systems.

Conclusions:

• The robust adaptive neural network controller is effective for electromagnetic suspension systems.
• The integration of command filtering and minimal learning parameters reduces computational complexity.
• The controller ensures robust stability and high performance under uncertainty and disturbances.