| Summary: | Powered assistive devices have been playing a major role in gait rehabilitation. Hereby, the development of time-effective control strategies to manage such devices is a key concern to rehabilitation engineering. This paper presents a real-time Feedback-Error Learning control strategy, by means of an Artificial Neural Network as a feedforward controller to acquire the inverse model of the plant, and a Proportional-Integral-Derivative feedback controller to guarantee stability and handle with disturbances. A Powered Knee Orthosis was used as the assistive device and a trajectory generator assistive strategy, previously acquired through an inertial system, was applied. A validation with one subject walking in a treadmill at 1 km/h with the Powered Knee Orthosis controlled by the Feedback-Error Learning control was performed. Evidences on the control behavior presented good performances, with the Artificial Neural Network taking 90 seconds to learn the inverse model, which enabled a decrease in the angular position error by 75% and eliminated the phase delay, when compared to solo Proportional-Integral-Derivative feedback controller. Robust reactions to external disturbances were also achieved. The implemented Feedback-Error Learning strategy proves to be a time-effective asset to control assistive powered devices.
|
|---|