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Although current humanoid controllers can rely on inverse kinematics or dynamics of the full humanoid system, powered prosthetic legs or assistive devices cannot, because they do not have access to the full states of the human system. This limitation creates the need for alternative control strategies. One strategy is to embed fundamental knowledge about legged dynamics and control in local feedback. In a previous paper, we have developed a control model of human locomotion which relies mostly on local feedback. The model can robustly walk at normal walking speeds. Here we extend this model to adapt to a wide range of walking speeds and to generate corresponding speed transitions. We use optimization of the model's control parameters and find key parameters responsible for steady walking between 0.8<i>ms<sup>-1</sup></i> and 1.8<i>ms<sup>-1</sup></i>, covering the range of speed at which humans normally walk. Using these parameters, we demonstrate speed transitions between the slow and fast walking. In addition, we discuss how the speed-dependent changes of the identified control parameters connect to biped walking dynamics, and suggest how these changes can be integrated in local feedback control.
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