Adaptive NN backstepping output-feedback control for stochastic nonlinear strict-feedback systems with time-varying...

Area of Science:

• Control Theory
• Nonlinear Systems
• Stochastic Systems
• Neural Networks

Background:

• Adaptive control is crucial for systems with uncertainties and time delays.
• Stochastic nonlinear systems with time-varying delays present significant control challenges.
• Existing methods often require complex designs or strong assumptions on system nonlinearities.

Purpose of the Study:

• To develop a novel adaptive output-feedback control strategy for uncertain stochastic nonlinear strict-feedback systems with time-varying delays.
• To utilize neural networks (NNs) for compensating unknown nonlinearities and time-varying delays.
• To simplify the control design compared to existing backstepping schemes.

Main Methods:

• Application of the circle criterion for nonlinear observer design.
• Employing a single neural network (NN) to approximate unknown nonlinear functions dependent on delayed system outputs.
• Development of an adaptive output-feedback control algorithm without imposing linear growth conditions on system nonlinearities.

Main Results:

• A new adaptive output-feedback control scheme is proposed for a challenging class of systems.
• The control design effectively handles uncertainties, stochasticity, nonlinearities, and time-varying delays.
• The proposed method demonstrates improved simplicity compared to existing neural network backstepping control techniques.

Conclusions:

• The developed control scheme provides an effective and simplified approach for adaptive output-feedback control of uncertain stochastic nonlinear systems with time-varying delays.
• The use of a single NN and the relaxation of growth conditions enhance the applicability and robustness of the control design.
• The effectiveness is validated through three illustrative examples.