Distributed Semiglobal Nash Equilibrium Seeking for Robotic Systems Subject to Unknown Disturbances
Xiongnan He, Zongli Lin · IEEE Transactions on Industrial Informatics · 2025
This article investigates distributed Nash equilibrium (NE) seeking for multirobot systems with nonlinear dynamics, time-varying disturbances, and individual inequality constraints under switching communication topologies. A novel control architecture is developed by integrating adaptive radial basis function (RBF) neural networks with projection-based pseudogradient dynamics. The proposed method enables each robot to estimate and track its local NE strategy in a fully distributed manner, without requiring global information or prior knowledge of the disturbances. Unlike existing methods that rely on static graphs or known disturbance bounds, our approach ensures constraint satisfaction and disturbance rejection simultaneously under a jointly strongly connected switching network. Numerical simulations involving five 2-degrees of freedom robotic manipulators demonstrate the effectiveness of the proposed strategy, achieving convergence to the NE within 20 s, strict adherence to inequality constraints.