Neural Network-Based Finite-Time Command Filtering Control for Switched Nonlinear Systems With Backlash-Like...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Switched nonlinear systems present significant control challenges due to arbitrary switching and hysteresis.
• Neural networks offer a powerful tool for approximating unknown nonlinear functions in complex systems.

Purpose of the Study:

• To develop a finite-time adaptive neural control strategy for switched nonlinear systems.
• To address challenges posed by arbitrary switching and hysteresis input.
• To improve tracking performance and ensure practical finite-time stability.

Main Methods:

• Utilized neural networks to approximate unknown nonlinear functions.
• Developed a new criterion for practical finite-time stability.
• Introduced improved error compensation signals and Levant differentiators.
• Employed a novel command filter backstepping technique for controller design.

Main Results:

• The proposed finite-time adaptive neural controller ensures tracking error convergence to a small neighborhood of the origin in finite time.
• The new stability criterion facilitates the design of effective finite-time control strategies.
• Simulation results validate the effectiveness of the proposed control method.

Conclusions:

• The presented finite-time adaptive neural control strategy is effective for switched nonlinear systems.
• The novel command filter backstepping technique enhances control performance and stability.
• This work contributes to the advancement of robust control for complex nonlinear systems.