For a kind of continuous-time nonlinear systems with uncertainties, a novel robust tracking control method is established based on critic learning formulation with single network. Firstly, an augmented system consisting of the tracking error and the reference trajectory is established, then the robust tracking control problem is transformed into a stabilization design problem. By adopting a cost function with a discount factor and a special utility term, the robust stabilization problem is transformed into an optimal control problem. Then, the optimal cost function is estimated by building a critic neural network, and consequently the optimal tracking control algorithm can be derived. In order to relax the initial admissible control conditions in the proposed algorithm, an extra term is added to the weight updating law of the critic neural network. Furthermore, the stability of the closed-loop system and the robust tracking performance are proved by the Lyapunov approach. Finally, the effectiveness and applicability of the developed approach are demonstrated via simulation results.