Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear systems often exhibit unmodeled dynamics and immeasurable states, posing significant control challenges.
• Traditional control methods struggle with non-lower triangular system structures and complex disturbances.

Purpose of the Study:

• To develop an adaptive neural output-feedback controller for nonlinear systems with unmodeled dynamics and non-lower triangular forms.
• To address difficulties arising from immeasurable states and structural inconsistencies in control signal design.

Main Methods:

• Design of a state observer for estimating system states.
• Application of virtual control signals and variable partition techniques.
• Development of an adaptive neural output-feedback controller using backstepping and Radial Basis Function (RBF) neural networks.
• Lyapunov stability analysis to guarantee system boundedness.

Main Results:

• The proposed controller ensures semiglobally and uniformly ultimate boundedness of all closed-loop system signals.
• Simulation results demonstrate rapid system convergence and bounded signal behavior.
• The controller effectively handles nonlinear disturbances that are functions of all system states.

Conclusions:

• The developed adaptive neural controller provides a robust solution for a class of non-lower triangular nonlinear systems with unmodeled dynamics.
• This work advances output-feedback control strategies for complex systems, offering a more general approach to disturbance handling.