New Super-Twisting Zeroing Neural-Dynamics Model for Tracking Control of Parallel Robots: A Finite-Time and Robust...

Area of Science:

• Robotics
• Control Systems
• Neural Networks

Background:

• Parallel robots require robust real-time tracking control in complex environments with disturbances.
• Conventional Zeroing Neural-Dynamics (ZND) models offer nonlinearity handling but struggle with unified disturbance rejection and finite-time convergence.

Purpose of the Study:

• To propose a novel Super-Twisting Zeroing Neural-Dynamics (ST-ZND) model.
• To address the limitations of conventional ZNDs in handling external disturbances and achieving finite-time convergence for parallel robot control.

Main Methods:

• Developed a novel ST-ZND model by integrating the Super-Twisting (ST) algorithm with ZND principles.
• Conducted rigorous theoretical analyses for global stability, finite-time convergence, and robustness against external disturbances.
• Validated the model through simulations and comparative studies on parallel robot tracking control tasks.

Main Results:

• The proposed ST-ZND model demonstrates global stability and finite-time convergence.
• The model exhibits superior robustness against external disturbances compared to conventional methods.
• Effectiveness and superiority were confirmed through illustrative examples and convergence tests.

Conclusions:

• The ST-ZND model provides a unified and effective framework for real-time tracking control of parallel robots.
• This novel approach significantly enhances robustness and convergence properties in the presence of external disturbances.
• The findings highlight the potential of ST-ZND for advanced robotic control applications.