Adaptive Neural-Network Controller for an Uncertain Rigid Manipulator With Input Saturation and Full-Order State...

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence in Engineering

Background:

• Robotic manipulators often face challenges like input saturation and unmodeled dynamics.
• Ensuring stability and performance under state constraints is crucial for practical applications.

Purpose of the Study:

• To develop an adaptive neural-network control scheme for rigid manipulators.
• To address input saturation, full-order state constraints, and unmodeled dynamics simultaneously.

Main Methods:

• An adaptive law using a multiply operation solution to mitigate input saturation effects.
• Neural networks for approximating unmodeled dynamics.
• Barrier Lyapunov functions to enforce input and state constraints.

Main Results:

• The adaptive law converges to a specified ratio, stabilizing the closed-loop system.
• Demonstrated uniform ultimate boundedness of all system states under constraints.
• Simulation results confirm the effectiveness of the control scheme in managing saturation and constraints.

Conclusions:

• The proposed adaptive neural-network control effectively handles input saturation and state constraints in rigid manipulators.
• The method ensures system stability and performance within predefined operational limits.
• This approach offers a robust solution for complex robotic control scenarios.