Robotic gait training is a promising tool for gait rehabilitation in people with neurological disorders. Including intuitive assessment and automatic adaptation of robotic assistance into robotic training is expected to further improve therapy outcomes. This contribution presents a novel performance-based adaptive controller, which adjusts robotic assistance based on the user's performance for diverse subtasks of gait. The resulting assistance profile of the algorithm could serve as an assessment tool or be used for monitoring progress during therapy. However, during training, values of gait speed and/or partial body weight support (PBWS) might vary. Therefore, the performance criteria should not depend on these factors to result in a reliable assessment. As a first step in deriving the potential of the controller as an assessment tool, ten healthy participants walked in the LOPES II robotic gait trainer testing the adaptive assistance at various gait speeds and levels of PBWS. Performances for all subtasks were dependent on the amount of PBWS. Therefore, the outcome of the novel control algorithm cannot directly be used as an assessment tool, but it has potential to be used for monitoring the progress of patients when the amount of PBWS and the speed are kept constant. Future studies will be focused on further testing the controller on people with neurological disorders to determine its potential as a monitoring tool.