In this paper, a new approach for high-precise force control in repeated tasks is proposed. In the general force control, the feedback control tends to be used for a matching the output force to the desired force safely. However, when the feedback control is used, the input of the plant does not change until the error is observed. The characteristic makes reactivity bad. In the position control, the model-based feedforward control is often used to solve this problem. However, the model-based feedforward control cannot be used in force control due to the difficulty of making the accurate model. The main reason of this problem is the uncertainty of the environmental impedance. Owing to the uncertainty, the model-based feedforward control cannot work properly. To solve this problem, Iterative Learning Control (ILC) is used in this paper. ILC does not require the accurate model, and it is able to match the output force to the desired one through repeated trials. However, the convergence condition in force control has not been analyzed until now. In this paper, by taking account of the environmental impedance of the object, a new convergence restriction is proposed. Under the proposed condition, the force control in repeated tasks becomes more precise. The procedure was demonstrated on simulations.