Implementation of a Trajectory Predictor and an Exponential Sliding Mode Controller on a Knee Exoskeleton Robot

In this article, trajectory generation, control and hardware development of a knee exoskeleton robot is provided. The robot aims to help the individuals with lower extremity weakness or disability during the sit-to-stand movement. In the trajectory generation phase, a new method is proposed which uses a library of sample trajectories to predict the sit-to-stand movement trajectory based on the initial sitting conditions of the user. This method utilizes the theory of "dynamic movement primitives" to estimate the sit-to-stand trajectory. The trajectory generation method is tested on a library of human motion data which has been obtained in a laboratory of motion analysis. In the next step, an exponential sliding mode controller is used to guide the robot along the predicted trajectory. The controller and the trajectory generator are implemented on the exoskeleton robot. For the hardware development, the xPC Target toolbox of MATLAB software and a data acquisition card was used. Finally, the robot was tested on a male adult. The subjects were asked to wear the robot while doing several sit-to-stand movements from various sitting positions. According to the results, the average power which is required to be applied by the user’s knee, is less when the exoskeletons assists him.