MNTZNN for Solving Hybrid Double-Deck Dynamic Nonlinear Equation System Applied to Robot Manipulator Control

Area of Science:

• Robotics
• Control Systems Engineering
• Computational Neuroscience

Background:

• Dynamic nonlinear equation systems are crucial for modeling complex real-world phenomena.
• Existing models struggle with multi-layered tasks and hybrid nonlinear constraints.
• Noise is a common challenge in practical applications, necessitating robust solutions.

Purpose of the Study:

• To propose a novel multilayered noise-tolerant zeroing neural network (MNTZNN) model.
• To address the limitations of conventional models in solving hybrid double-deck dynamic nonlinear equation systems (H3DNES).
• To enhance robustness against noise in dynamic nonlinear system solutions.

Main Methods:

• Development of a new zeroing neural network (ZNN) design formula.
• Formulation of the MNTZNN model capable of handling second-order derivative equations.
• Mathematical proof of the model's robustness under specific parameter constraints.

Main Results:

• The MNTZNN model successfully solves H3DNES even with noise in both layers.
• Demonstrated strong robustness and programmable error bounds for the MNTZNN model.
• Successful application to robot manipulator tracking control problems.

Conclusions:

• The MNTZNN model offers a robust and effective solution for H3DNES.
• The proposed ZNN design enhances model robustness and adaptability.
• The MNTZNN model shows significant potential for advanced robotics and control applications.