Globally Exponentially Stable Tracking Control of Self-Restructuring Nonlinear Systems.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Existing neural network (NN)-based control methods often overlook NN reliability, leading to semiglobal stability guarantees.
• High-order, nonaffine dynamic systems with uncertainties and self-restructuring nonlinearities present significant control challenges.

Purpose of the Study:

• To develop a robust NN-based tracking control approach for uncertain high-order self-restructuring nonaffine dynamic systems.
• To ensure globally stable tracking by addressing the reliable operation of NN approximation units.

Main Methods:

• A cooperative control strategy combining a safeguard control unit and an NN-based control unit.
• The safeguard control unit drives system states into a stable region for safe NN activation.
• The NN-based control unit provides reliable, globally stable tracking control.

Main Results:

• The proposed method ensures tracking error enters a stable region within finite time.
• Exponentially globally stable tracking is achieved, surpassing uniformly ultimately bounded (UUB) results.
• The approach guarantees global zero-error tracking for systems with fixed and self-restructuring nonlinearities.

Conclusions:

• The novel cooperative control strategy effectively ensures reliable and globally stable tracking for complex dynamic systems.
• The integration of safeguard and NN-based control provides a robust solution for systems previously challenging to control.
• Theoretical analysis and numerical simulations validate the proposed method's effectiveness.