Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems.

Area of Science:

• Control Theory
• Nonlinear Systems
• Stochastic Systems

Background:

• Nonstrict feedback stochastic nonlinear systems present complex control challenges.
• Existing methods often require knowledge of unknown virtual control coefficients (UVCC).

Purpose of the Study:

• To design an adaptive neural network asymptotic tracking controller for nonstrict feedback stochastic nonlinear systems.
• To overcome the limitation of unknown virtual control coefficients in control design.

Main Methods:

• Utilizing a backstepping algorithm combined with approximation-based neural networks.
• Employing a bound estimation scheme and smooth functions for controller construction.
• Applying Lyapunov functions and inequalities to ensure stability and convergence.

Main Results:

• Successfully designed an asymptotic tracking controller that does not require prior knowledge of UVCC.
• Demonstrated the asymptotic convergence and stability of the closed-loop system under stochastic disturbances.
• Validated the theoretical findings through a simulation example.

Conclusions:

• The proposed adaptive neural network control strategy effectively addresses the asymptotic tracking problem for nonstrict feedback stochastic nonlinear systems.
• The method successfully overcomes the challenge of unknown virtual control coefficients.
• The approach ensures system stability and asymptotic convergence in the presence of stochastic disturbances.