Robust output feedback control of nonlinear stochastic systems using neural networks

Area of Science:

• Control Theory
• Artificial Intelligence
• Nonlinear Systems

Background:

• Uncertain stochastic nonlinear systems pose significant control challenges.
• Approximating complex nonlinearities is crucial for effective control design.
• Adaptive control strategies are needed to handle system uncertainties.

Purpose of the Study:

• To develop an adaptive output feedback controller for uncertain stochastic nonlinear systems.
• To utilize neural networks for approximating system nonlinearities.
• To enhance controller robustness and performance through adaptive tuning and robust design.

Main Methods:

• Decomposition of nonlinearities into neural network approximations and uncertainties.
• Adaptive tuning of neural network weights using Lyapunov design.
• Robust optimal controller design incorporating parameter projection, control saturation, and high-gain observers.

Main Results:

• The proposed controller effectively manages uncertain stochastic nonlinear systems.
• Neural network-based approximation accurately captures system nonlinearities.
• Simulations demonstrate high performance and significant tolerance to large errors.

Conclusions:

• The adaptive output feedback controller provides a robust solution for uncertain stochastic nonlinear systems.
• The integration of neural networks and Lyapunov design enables effective adaptive control.
• The controller's performance is validated through simulation, showing resilience to substantial errors.