Finite-Time Robust Intelligent Control of Strict-Feedback Nonlinear Systems With Flight Dynamics Application

Area of Science:

• Control Systems Engineering
• Robotics
• Artificial Intelligence

Background:

• Uncertain strict-feedback systems pose challenges for traditional control methods.
• Achieving precise tracking and maintaining stability under uncertainty requires advanced control strategies.

Purpose of the Study:

• To develop a robust tracking control for uncertain strict-feedback systems.
• To enhance adaptation precision and ensure system stability using a hybrid approach.
• To address output constraints effectively within the control design.

Main Methods:

• A switching mechanism combines robust and adaptive control designs.
• A finite-time robust design ensures stability outside the adaptive region.
• A novel prediction error-based adaptive law optimizes estimation performance.
• Barrier Lyapunov functions are integrated to handle output constraints.

Main Results:

• Guaranteed finite-time convergence and uniform ultimate boundedness of system signals.
• The proposed controller demonstrates robustness against system uncertainties.
• Effective adaptation and high tracking precision are achieved within the neural working domain.

Conclusions:

• The hybrid robust and adaptive control strategy effectively manages uncertain strict-feedback systems.
• The integration of prediction error-based adaptation and barrier Lyapunov functions enhances performance and stability.
• Simulation results validate the robustness and adaptive capabilities of the proposed control approach.