A priori guaranteed evolution within the neural network approximation set and robustness expansion via prescribed...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Strict feedback systems often face challenges with unknown nonlinearities and external disturbances.
• Existing neural network control methods struggle to guarantee system states remain within effective approximation regions.
• Ensuring bounded closed-loop signals and prescribed performance is a persistent control problem.

Purpose of the Study:

• To design a neuroadaptive control scheme for strict feedback systems.
• To achieve prescribed performance guarantees for output error.
• To ensure all closed-loop signals remain bounded despite system uncertainties.

Main Methods:

• A constructive methodology is employed to address limitations in neural network control.
• The approach guarantees system states evolve within a compact region for reliable neural network approximation.
• The control scheme is designed to handle unknown system nonlinearities and external disturbances.

Main Results:

• The designed neuroadaptive control scheme achieves prescribed performance guarantees.
• All closed-loop signals are proven to be bounded.
• Robustness against external disturbances is significantly enhanced.
• The method avoids special initialization or complex control gain selection.

Conclusions:

• The proposed neuroadaptive control scheme offers a robust solution for strict feedback systems.
• It effectively manages unknown nonlinearities and disturbances while ensuring stability and performance.
• The constructive methodology overcomes key challenges in applying neural networks to control problems.