Abstract: The near-optimal stabilization problem for nonlinear constrained systems is solved by greedy iterative DHP (Dual heuristic programming) algorithm. Considering the control constraint of the system, a nonquadratic functional is first introduced in order to transform the constrained problem into a unconstrained problem. Then based on the costate function, the greedy iterative DHP algorithm is proposed to solve the Hamilton-Jacobi-Bellman (HJB) equation of the system. At each step of the iterative algorithm, a neural network is utilized to approximate the costate function, and then the optimal control policy of the system can be computed directly according to the costate function, which removes the action network appearing in the ordinary approximate dynamic programming (ADP) method. Finally, two examples are given to demonstrate the validity and feasibility of the proposed optimal control scheme.