Cooperative Control for Multirobot Systems With Partially Unknown Structural Characteristics: A Game-Theoretic Perspective

Structural characteristics play a pivotal role in the cooperative control of multirobot systems. Although the gravity vector can often be obtained through precalibration, accurately determining the Jacobian, mass, and Coriolis matrices remains challenging because of structural variations and parameter uncertainties, which complicates precise control. This challenge can be reconceptualized as a game of incomplete information, in which each robot, acting as a player, operates with limited knowledge of its structural characteristics. To address this informational deficit, a kinematics- and dynamics-based scheme is proposed to efficiently estimate the Jacobian, mass, and Coriolis matrices of multirobot systems with partially unknown structural characteristics (MRSPUSC) using data-driven techniques. With these key structural matrices continuously estimated, a discrete-time neural dynamics model is then developed to search for the optimal strategy corresponding to the Nash equilibrium of the game. The resulting integrated scheme enables the effective control of MRSPUSC and demonstrates the capability of the proposed approach to overcome challenges arising from partially unknown structural characteristics