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.