DSC-based RBF neural network control for nonlinear time-delay systems with time-varying full state constraints

Area of Science:

• Control Systems Engineering
• Nonlinear Control Theory
• Systems Stability Analysis

Background:

• Stabilizing uncertain systems with time delays is challenging.
• Maintaining state variables within dynamic constraints is crucial for system performance.
• Existing methods may struggle with combined uncertainties and time delays.

Purpose of the Study:

• To develop a robust control scheme for uncertain time-delayed systems.
• To ensure all system states adhere to time-varying constraints.
• To achieve precise tracking performance and bounded system signals.

Main Methods:

• Utilized backstepping combined with dynamic surface control for controller design.
• Employed radial basis function neural networks to handle system uncertainties.
• Incorporated time-varying barrier Lyapunov functions to enforce state constraints.
• Applied Lyapunov-Krasovskii functionals to manage time-delay effects.

Main Results:

• Successfully stabilized uncertain time-delayed systems.
• Ensured all state variables remained within predefined time-varying boundaries.
• Achieved satisfactory tracking performance for the desired output.
• Demonstrated boundedness of all signals within the closed-loop system.
• Validated the control scheme's effectiveness through simulation.

Conclusions:

• The proposed control scheme effectively stabilizes uncertain time-delayed systems under time-varying constraints.
• The integration of advanced control techniques ensures robust performance and signal boundedness.
• The method offers a reliable approach for complex control problems in engineering applications.