Neurodynamics for Orthogonal Repetitive Motion of Multiple-Mobile Manipulators Based on a Distributed Scheme: A...

Area of Science:

• Robotics and Control Systems
• Distributed Systems
• Optimization Theory

Background:

• Effective kinematic redundancy utilization is crucial for multiple omnidirectional mobile redundant manipulators (MOMRMs) in task-oriented applications.
• Existing distributed control schemes often neglect communication noise and joint drift, impacting robustness and coordination.
• Joint drift affects stability and motion consistency in repetitive tasks.

Purpose of the Study:

• To develop a hybrid optimization framework and an anti-disturbance distributed cooperative control strategy for MOMRMs.
• To enhance system robustness, coordination efficiency, and motion consistency.
• To suppress communication noise and mitigate joint drift.

Main Methods:

• A hybrid multiobjective optimization framework integrating orthogonal repetitive motion planning and joint velocity optimization.
• An anti-disturbance distributed cooperative control strategy based on game theory.
• A fuzzy adaptive dual-input double-integral noise-resistant neural dynamics (FADINRND) model to approximate Nash equilibria.

Main Results:

• The proposed FADINRND model effectively suppresses linear and quadratic disturbances, outperforming existing neurodynamic solvers.
• The control strategy enhances system robustness and coordination efficiency in the presence of communication noise.
• Theoretical proofs confirm the convergence and stability of the FADINRND model.

Conclusions:

• The developed hybrid framework and anti-disturbance control strategy significantly improve the performance of MOMRMs.
• The FADINRND model offers superior noise resistance and faster convergence for distributed strategy computation.
• The findings are validated through simulations and experimental platform tests, demonstrating practical applicability.