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When the simplest model of robot is in the process of walking, the feet of its swinging legs may brush the floor. In view of this problem, a model to shorten the swinging legs when the knees bend is proposed. When the swinging leg begins to swing, the knee of the swing leg bends and gets locked and the swing leg becomes shortened; when the swing leg swings to the largest position, the knee becomes unlocked and then locked after the swing leg unbends. In this condition, the swing leg swings back and the system becomes straight leg model. The heel impact control is adopted, so that before the swinging leg goes to the ground, an instantaneous wallop on to the hip can be applied at the place where the dragged support leg connect with the ground. The impact can reduce the energy loss of swinging leg landing on the ground, and can also drive the passive robot to walk forward. Iterative learning control algorithm is designed to find the limit cycle and the fixed point; through simulation, the impact forces under the tracking of different given expectation steps are calculated. The simulation results show that the iterative learning control can effectively implement the tracking of different expectation steps, quickly find the fixed points of robot system, and get a stable limit cycle through the convergence of the phase plane, to ensure the stable walking process of the robot.