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Based on adaptive dynamic programming, a novel suboptimal control method for a class of nonlinear singularly perturbed systems is proposed. According to the slow/fast Hamilton-Jacobi-Bellman(HJB) equations, the initial performances converge to the optimal performances by neural network approximation and the iteration between control laws and performance indices. It avoids solving the complex HJB equations directly. The convergence of the algorithm is proved and the obtained composite control is shown to be suboptimal. Simulation results demonstrate the effectiveness.