Adaptive fuzzy logic control for a robotic gait training orthosis

This paper aims to develop an adaptive control strategy for a fuzzy logic system for a robotic gait training orthosis whose design is bio-inspired from biomechanics of the human gait. Powerful yet light weight pneumatic muscle actuators (PMAs) fulfilled the ambulatory requirements of the robot while also providing the actuation required to create the sagittal plane rotations at the hip and knee. The PMA is controlled by a fuzzy logic controller based on Mamdani inference for obtaining the rotational degrees of freedom. To cope with the nonlinear behaviour of the PMA towards external disturbances, a second fuzzy-based controller was also developed. In order to compensate for time dependent characteristics of the PMA, an adaptive control mechanism was introduced. Experiments were conducted on healthy subjects for understanding and estimating the performance of the adaptive fuzzy logic controller and robotic design. The human-robot interaction was mainly passive-active, while the paths were predefined.