Asymptotic Fuzzy Neural Network Control for Pure-Feedback Stochastic Systems Based on a Semi-Nussbaum Function...

Area of Science:

• Control Theory
• Artificial Intelligence
• Stochastic Systems

Background:

• Existing control methods for stochastic systems often yield only bounded tracking errors.
• Pure-feedback stochastic systems present unique control challenges.

Purpose of the Study:

• To develop an advanced control strategy for pure-feedback stochastic systems.
• To achieve asymptotic stability in probability for tracking errors, moving beyond bounded results.

Main Methods:

• Proposed a novel semi-Nussbaum function-based technique.
• Employed adaptive backstepping controller design.
• Integrated the Nussbaum function with adaptive control.

Main Results:

• The developed controller guarantees asymptotic stability in probability for tracking errors.
• The novel semi-Nussbaum function effectively addresses control limitations.

Conclusions:

• The proposed fuzzy neural network control offers a significant advancement for pure-feedback stochastic systems.
• This method provides a more robust and precise control solution compared to existing approaches.