Prescribed Finite-Time Adaptive Neural Tracking Control for Nonlinear State-Constrained Systems: Barrier Function...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear systems often face challenges with time-varying state constraints.
• Existing control methods may struggle to guarantee performance under such constraints.
• Adaptive neural networks offer potential for handling complex system dynamics.

Purpose of the Study:

• To develop a novel backstepping-based adaptive neural tracking control design.
• To address nonlinear systems with time-varying state constraints.
• To ensure virtual control signals adhere to constraints and output errors converge precisely.

Main Methods:

• Utilizing a backstepping design procedure.
• Employing adaptive neural networks for function approximation.
• Integrating barrier Lyapunov functions to handle state constraints.
• Combining adaptive neural backstepping with barrier Lyapunov functions.

Main Results:

• The proposed controller ensures virtual control signals respect state constraints.
• The output tracking error converges to a small neighborhood of the origin.
• Convergence is achieved within a prescribed finite time and accuracy.
• Simulation examples validate the effectiveness of the control scheme.

Conclusions:

• The novel adaptive neural tracking controller effectively manages nonlinear systems with time-varying constraints.
• The barrier Lyapunov function approach successfully enforces state constraints.
• The controller guarantees finite-time convergence of the output tracking error to a desired level.