Observer-Based Neural Control of N-Link Flexible-Joint Robots

Area of Science:

• Robotics
• Control Systems Engineering
• Artificial Intelligence

Background:

• Flexible-joint robots present complex control challenges due to inherent nonlinearities and unmeasured states.
• Traditional control methods often require full state information, which can be difficult or costly to obtain.
• Adaptive control and neural networks offer potential solutions for handling uncertainties and estimating unmeasured variables.

Purpose of the Study:

• To develop an observer-based adaptive neural control strategy for n-link flexible-joint electrically driven robots.
• To design a control system that relies solely on position and armature current measurements.
• To ensure robust and stable control performance despite unknown system nonlinearities.

Main Methods:

• An adaptive observer is designed to estimate unmeasured link and motor velocities.
• Radial basis function neural networks (RBFNNs) are employed to approximate unknown robot dynamics.
• Backstepping techniques combined with Lyapunov stability theory are used for controller design.
• An event-triggered mechanism is incorporated to reduce communication and computation load.

Main Results:

• The proposed control strategy ensures that all system signals are semi-globally ultimately uniformly bounded.
• Tracking errors are demonstrated to converge to a small neighborhood of zero, indicating precise control.
• The adaptive observer successfully estimates unmeasured velocities.
• The neural network effectively approximates the robot's nonlinear dynamics.

Conclusions:

• The developed observer-based adaptive neural control strategy effectively manages n-link flexible-joint robots using limited sensor data.
• The event-triggered approach enhances efficiency while maintaining robust stability and accurate tracking.
• Simulation results validate the efficacy and practical applicability of the proposed control system.