Neural network based tracking control of a flexible macro-micro manipulator system

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Flexible manipulator systems present complex dynamic challenges for precise control.
• Accurate dynamic modeling is often difficult or impossible for such systems.
• Existing control methods may require extensive system identification or lack robustness.

Purpose of the Study:

• To develop a stable tracking control strategy for flexible macro-micro manipulator (M(3)) systems.
• To design controllers for macro and micro arms without prior knowledge of the M(3) system's dynamic model.
• To demonstrate the effectiveness of a neural network-based approach using Lyapunov stability theory.

Main Methods:

• A two-layer neural network was employed to approximate the nonlinear dynamics of the M(3) system.
• A learning algorithm based on Lyapunov stability theory was derived for the neural network.
• Controllers for both macro and micro arms were developed concurrently with the network training.
• Simulations were conducted to evaluate the proposed control scheme.

Main Results:

• The proposed control scheme ensures that both tracking errors and weight-tuning errors are uniformly ultimately bounded.
• The neural network successfully approximated the complex nonlinear dynamics of the flexible M(3) system.
• The developed controllers achieved stable tracking performance.

Conclusions:

• The presented neural network-based control approach offers a robust solution for stable tracking control of flexible M(3) systems.
• This method eliminates the need for a priori dynamic model information, enhancing its practical applicability.
• The simulation results validate the superiority of this approach over conventional Proportional-Derivative (PD) control.