Global tracking control of strict-feedback systems using neural networks.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence in Control
• Nonlinear System Dynamics

Background:

• Existing adaptive neural controllers often require the neural approximation to be valid indefinitely, a condition difficult to guarantee in practice.
• This limitation can lead to performance degradation or instability in real-world applications.
• Current robust controller additions are typically limited to systems with matched uncertainties.

Purpose of the Study:

• To extend adaptive neural control to strict-feedback systems with mismatched uncertainties.
• To develop a robust yet adaptive control strategy that guarantees stability and performance.
• To overcome the limitations of existing methods by incorporating smooth switching between controllers.

Main Methods:

• Development of a multiswitching-based backstepping methodology.
• Integration of adaptive neural controllers and robust controllers that switch smoothly.
• Application of the backstepping framework to handle strict-feedback systems with mismatched uncertainties.

Main Results:

• The proposed controller ensures globally uniform ultimate boundedness for the system.
• The control strategy effectively handles mismatched uncertainties, a significant extension from prior work.
• The design successfully avoids potential control singularities, enhancing practical applicability.

Conclusions:

• The multiswitching adaptive neural controller provides a robust and stable solution for strict-feedback systems with challenging uncertainties.
• The smooth switching mechanism is key to integrating adaptive and robust control within the backstepping framework.
• Simulation results validate the effectiveness and stability guarantees of the proposed control design.