Transparent control is still highly challenging for robotic exoskeletons, especially when a simple strategy is expected for a large-impedance device. To improve the transparency for late-phase rehabilitation when "patient-in-charge" mode is necessary, this paper aims at adaptive identification of human motor intent, and proposed an iterative prediction-compensation motion control scheme for an exoskeleton knee joint. Based on the analysis of human-machine interactive mechanism (HMIM) and the semiphenomenological biomechanical model of muscle, an online adaptive predicting controller is designed using a focused time-delay neural network (FTDNN) with the inputs of electromyography (EMG), position and interactive force, where the activation level of muscle is estimated from EMG using a novel energy kernel method. The compensating controller is designed using the normative force-position control paradigm. Initial experiments on the human-machine integrated knee system validated the effectiveness and ease of use of the proposed control scheme.