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

In task-oriented applications of multiple omnidirectional mobile redundant manipulators (MOMRMs), effectively utilizing kinematic redundancy is essential for enhancing system performance while ensuring accurate task execution. However, most existing distributed control schemes overlook the influence of communication noise on cooperative behaviors, which consequently undermines system robustness and coordination efficiency. Meanwhile, joint drift remains a long-standing issue in repetitive motion tasks, further affecting system stability and motion consistency. To address these challenges, a hybrid multiobjective optimization framework is developed that integrates orthogonal repetitive motion planning with joint velocity optimization, and an anti-disturbance distributed cooperative control strategy based on game theory is introduced to suppress communication noise and enable independent neighborhood decision-making. To compute for distributed strategies, a fuzzy adaptive dual-input double-integral noise-resistant neural dynamics (FADINRND) model is proposed to approximate Nash equilibria. Different from existing neurodynamic solvers with limited noise-rejection capability, the proposed model suppresses challenging linear and quadratic disturbances, thereby improving convergence rate and system robustness. The convergence and stability of the proposed model are rigorously proven theoretically. The effectiveness and superiority of the scheme and model are verified through numerical simulation and platform experiments.