Time-Varying Momentum-Like Neurodynamic Optimization Approaches With Fixed-Time Convergence for Nash Equilibrium Seeking in Noncooperative Games
提出了几种新的时变动量类神经动力学方法，用于求解非合作博弈的纳什均衡，能在固定时间内从任意初始状态收敛，并给出了收敛时间上界，通过能源消耗博弈仿真验证了其优越性和实用性。
In this article, several novel time-varying momentum-like neurodynamic optimization approaches are proposed for Nash equilibrium (NE) seeking of noncooperative games. It is shown that the dynamics trajectories converge to NE within fixed-time from arbitrary initial conditions, achieving a quicker convergence rate through the selection of distinct time-varying coefficients. Moreover, the upper bounds of the settling time for the proposed NE seeking neurodynamic approaches are explicitly provided. In addition, the study investigates the robustness of the designed neurodynamic approaches in the presence of bounded noises. The superior convergence properties and practicability of our approaches are demonstrated through a simulation example involving energy consumption games.