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Integrating humans and robotics technology into one system offers multiple opportunities for creating assistive technologies that can be employed in biomechanical, industrial, and aerospace applications. The present study deals with employing the adaptive neuro-fuzzy inference system (ANFIS) approach in rule base derivation for powered exoskeleton intelligent control to assist paraplegic patient mobility. By employing the hybrid learning algorithm, optimal distributed membership functions to describe the mapping relation in the input and output parameters of the gait cycle were derived. As the proposed control strategy was unaffected by changing human dynamics, the reliability and robustness of the controller for safe interaction with humans were increased.