Finite-Time Model-Free Adaptive Consensus Control for Unknown Nonlinear Multiagent Systems With Experimental Validation
针对现有无模型自适应一致性控制只能实现渐近跟踪的局限，提出一种有限时间方法，使未知非线性多智能体系统快速达成一致性跟踪，并通过仿真和实验验证了优越性能。
Almost all existing model-free adaptive consensus control (MFACC) strategies for nonlinear multiagent systems (MASs) have been limited to asymptotic tracking performance. To address this limitation and unify prior theoretical frameworks, this article proposes a novel finite-time MFACC (FMFACC) approach for rapid consensus tracking in nonlinear MASs with completely unknown dynamics. A new finite-time consensus tracking error is first constructed by incorporating a variable proportional coefficient with an adjacent maximum tracking error factor, ensuring finite-time convergence while accounting for output couplings. Building on this and by establishing time-varying data models that capture the unknown nonlinear dynamics, a distributed finite-time consensus tracking control law with a data-driven adaptive gain matrix is developed, enabling model-free fast coordination of all agents along a predefined trajectory. Furthermore, by employing an equivalent system transformation strategy, the relationship between the resulting closed-loop nonlinear FMFACC system and its conventional linear counterpart is rigorously analyzed, proving asymptotically finite-time consensus tracking despite unknown system dynamics and output couplings. Finally, simulation and experimental studies conclusively demonstrate the superior performance of the proposed FMFACC approach.