Robust Zeroing Neural-Dynamics and Its Time-Varying Disturbances Suppression Model Applied to Mobile Robot...

Area of Science:

• Robotics
• Control Systems
• Computational Neuroscience

Background:

• Mobile robot manipulators require precise inverse kinematics solutions for effective operation.
• Existing neural network models often lack robustness against external time-varying disturbances.
• Addressing these disturbances is crucial for reliable robotic control.

Purpose of the Study:

• To propose a novel robust zeroing neural-dynamics (RZND) approach for solving the inverse kinematics problem of mobile robot manipulators.
• To investigate the suppression of four common forms of time-varying disturbances using the RZND model.
• To provide theoretical analysis and empirical validation of the RZND model's antidisturbance performance.

Main Methods:

• Development of a novel robust zeroing neural-dynamics (RZND) model.
• Investigation and suppression of four common types of time-varying external disturbances.
• Theoretical analysis of antidisturbance performance and convergence properties.
• Comparative simulations against conventional Zhang neural network and gradient-based recurrent neural network models.

Main Results:

• The proposed RZND model effectively suppresses time-varying disturbances.
• The RZND model converges to the exact solution for inverse kinematics with bounded or zero steady-state error.
• Simulation studies demonstrate superior performance, robustness, and efficacy compared to existing methods.
• The model exhibits strong antidisturbance capabilities against various external factors.

Conclusions:

• The novel RZND approach offers a robust and effective solution for the inverse kinematics of mobile robot manipulators.
• The RZND model demonstrates significant advantages in handling time-varying disturbances, outperforming existing neural network models.
• This work provides a reliable method for enhancing the precision and stability of mobile robot manipulator control systems.