When multi-agent systems are subject to time-varying parameter uncertainties, traditional fixed-gain control strategies often face the challenge of balancing convergence speed against control accuracy and exhibit limited robustness. To overcome these limitations, this paper proposes a robust adaptive control strategy to improve the system's consensus tracking performance in environments with dynamically varying parameters. Firstly, a graph-structured consensus error is formulated based on the fixed communication topology. For each agent, this error is defined as the weighted sum of its state deviations from its neighboring agents and the leader. Subsequently, a distributed control law is designed, which incorporates a parameter adaptation mechanism and an optimization strategy based on the gradient of a global cost function, enabling online tuning of the control gains. Furthermore, based on the Lyapunov stability theory, the uniformly ultimately boundedness of the closed-loop system is rigorously proven. Simulation results further demonstrate that, under a fixed communication topology and in the presence of parametric disturbances, the proposed strategy achieves rapid and smooth state convergence, effectively improving both the tracking performance and robustness of the system.