Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence

Background:

• Strict-feedback nonlinear systems are challenging to control due to inherent complexities.
• Unknown time delays and uncertain parameters further complicate system stability analysis and control design.
• Adaptive control strategies are crucial for handling system uncertainties in real-world applications.

Purpose of the Study:

• To develop an adaptive neural control strategy for strict-feedback nonlinear systems with unknown time delays.
• To address the challenge of unknown virtual control coefficient signs in the control design.
• To ensure the stability and performance of the closed-loop system despite uncertainties.

Main Methods:

• Adaptive neural control utilizing backstepping design.
• Lyapunov-Krasovskii functionals for compensating unknown time delays.
• Neural networks to approximate unknown system functions and adapt control parameters.

Main Results:

• The proposed control method guarantees semiglobal uniformly ultimately boundedness of all closed-loop signals.
• The system output is proven to converge to a small neighborhood of the origin.
• Simulation results demonstrate the effectiveness of the adaptive neural control approach.

Conclusions:

• The developed adaptive neural control is effective for strict-feedback nonlinear systems with unknown time delays.
• The method's ability to handle unknown coefficient signs and time delays offers a robust solution.
• This approach advances the control of complex nonlinear systems in the presence of uncertainties.