With over 600, 000 people each year surviving a stroke, it has become the leading cause of serious long-term disability in the United States [1,2,3], Studies have proven that through repetitive task training, neural networks can be re-mapped thus increasing the mobility of the patient [4-8], This paper is a continuation of Kartik Bharadwaj's and Arizona State University's research on the Robotic Gait Trainer , This work is funded in part by the National Institutes of Health (NIH), grant number -1 R43 HD04067 01. Previously the gait cycle was fixed at two seconds. For a smooth gait the patient had to be trained to follow the frequency of the robot. Audible cues were sounded to help the patient keep rhythm with the robot. This device now has an updated control methodology based on a Matlab and Simulink platform that allows the robot to dynamically adjust to the patient's pace of gait. Data collected from an able-bodied person walking with the new device showed that the device dynamically adjusted to any normal range of walking gait. This more flexible design will allow the patient to focus more on the therapy and walk at his/her natural pace.