Logic-Adaptive Discrete Neural Dynamics for Distributed Cooperative Control of Multirobot Systems via Minimum Infinity Norm Optimization

Abstract

In pursuit of safe and efficient distributed cooperative control of multi-robot systems (MRSs), a logic-adaptive discrete neural dynamics (LADND)-based minimum infinity norm (MIN) strategy is introduced in this paper. The MIN strategy is employed to address critical safety concerns caused by excessively high velocity in the individual joint. To further improve the adaptivity of the neural dynamics solver, a fuzzy system is integrated to enable adaptive parameter adjustment based on the real-time behavior of MRSs. Specifically, the cooperative control problem is formulated as a linear program incorporating the MIN along with constraints associated with distributed network topology and orientation maintenance, thereby enhancing the safety and effectiveness of MRSs. To efficiently solve the proposed linear program, an LADND solver is developed, which adaptively adjusts its parameter in real time according to the trajectory tracking error and its derivative. Furthermore, theoretical analyses confirm the convergence and robustness of the proposed LADND solver. Simulative and experimental results validate the effectiveness of the proposed LADND-based MIN strategy in cooperative trajectory tracking tasks.