Fixed-time adaptive neural network control for nonstrict-feedback nonlinear systems with deadzone and output...

Area of Science:

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

Background:

• Nonstrict-feedback nonlinear systems often exhibit complex behaviors, including deadzones and output constraints.
• Existing control methods may struggle with fixed-time convergence, unknown nonlinearities, and computational complexity.
• Addressing these challenges is crucial for robust and efficient control applications.

Purpose of the Study:

• To develop a fixed-time control strategy for nonlinear systems with deadzone and output constraints.
• To approximate unknown nonlinear functions using radial basis function neural networks (RBFNN).
• To ensure fixed-time convergence of tracking errors and boundedness of all closed-loop signals.

Main Methods:

• Construction of a tan-type Barrier Lyapunov function (BLF) to manage output constraints.
• Approximation of unknown nonlinearities using radial basis function neural networks (RBFNN).
• Design of virtual and actual control inputs using backstepping, fixed-time differentiators, and deadzone inverse techniques.

Main Results:

• Guaranteed fixed-time convergence of the tracking error to a small neighborhood around zero.
• All closed-loop signals are proven to remain bounded.
• The proposed control strategy effectively handles algebraic-loop issues and reduces computational burden.

Conclusions:

• The developed control scheme offers an effective solution for fixed-time control of complex nonlinear systems.
• The method demonstrates reduced complexity and fewer adaptation parameters, enhancing practical implementation.
• Validated through simulations on academic, electromechanical, and aircraft systems, proving its effectiveness.