Neural Network-Based Adaptive Boundary Control of a Flexible Riser With Input Deadzone and Output Constraint

Area of Science:

• Ocean Engineering
• Control Systems
• Nonlinear Dynamics

Background:

• Riser systems in offshore engineering face challenges from system uncertainties, input deadzones, and output constraints.
• Ensuring precise control and stability in these systems is crucial for operational integrity.

Purpose of the Study:

• To develop an advanced control strategy for vibration abatement in riser systems.
• To address system uncertainties, input deadzone nonlinearities, and output constraints simultaneously.

Main Methods:

• A boundary control law was designed using the backstepping method and Lyapunov's theory.
• A barrier Lyapunov function was employed to guarantee output constraints.
• Adaptive neural networks were utilized to handle system uncertainties and deadzone compensation.

Main Results:

• The developed controller successfully satisfied output constraints.
• System stability was rigorously guaranteed through Lyapunov synthesis.
• Numerical simulations demonstrated the effectiveness of the adaptive neural network boundary control law.

Conclusions:

• The proposed adaptive neural network boundary control law is effective for vibration abatement in riser systems.
• The control strategy robustly handles system uncertainties and nonlinearities.
• This approach offers a promising solution for enhancing the performance and safety of riser systems.