Adaptive Neural Synchronization Control for Bilateral Teleoperation Systems With Time Delay and Backlash-Like...

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Bilateral teleoperation systems face challenges with time delays and backlash-like hysteresis, impacting synchronization accuracy.
• Existing control methods often require complex hysteresis inverse models, limiting practical application.

Purpose of the Study:

• To develop advanced adaptive neural control strategies for robust master-slave synchronization in teleoperation.
• To address the limitations of time delay and hysteresis without relying on hysteresis inverse.

Main Methods:

• Utilized radial basis functions neural networks for their approximation capabilities.
• Developed two improved adaptive neural control schemes based on Lyapunov stability analysis.
• Employed matrix norm of neural network weight vectors as estimated parameters.

Main Results:

• Guaranteed convergence of position and velocity tracking errors to a small neighborhood of the origin.
• Demonstrated the effectiveness of the proposed control schemes through simulations.
• Eliminated the need for a hysteresis inverse in the control design.

Conclusions:

• The novel adaptive neural control approaches effectively manage time delays and hysteresis in bilateral teleoperation.
• The proposed methods offer a simplified and robust solution for teleoperation synchronization.
• Simulation results validate the superior performance and stability of the developed controllers.