Fuzzy-Based Optimization and Control of a Soft Exosuit for Compliant Robot-Human-Environment Interaction

Many previous studies of soft exosuits improved human locomotion performance. However, there is no example to control a soft exosuit using human ankle impedance adaption in assistance tasks compliantly. In this article, the human-environment interaction information is exploited into the exosuit control. A novel fuzzy-based optimization and control method of soft exosuit is proposed to provide plantarflexion assistance for human walking by changing the human-robot interaction. In particular, a fuzzy neurodynamics optimization is developed to learn the unknown human ankle impedance parameters automatically. A fuzzy approximation technique is applied to improve the control performance of the exosuit when a human is walking with unknown human-robot interaction model parameters. This control scheme guarantees that the human-robot dynamics follows a target human ankle impedance model to obtain the compliant interaction performance. Experiments on different participants verify the effectiveness of the control scheme. Results show that a compliant human-robot interaction is achieved by learning the human-environment interaction parameters, i.e., the human ankle parameters. It indicates that our proposed method can facilitate exosuit control to achieve compliant robot-human-environment interaction.