This paper proposed a walking assistance method by using trunk rotation based on the spinal engine theory. According to the spinal engine theory, our walking is realized by cooperation between upper body and legs through the energy exchange. Therefore, we developed an assistance device for trunk rotation and its control method. We adopted the periodic input control as the control method and modified it to be suitable for human assistance. According to experimental results, the proposed method realized walking assist by supporting trunk rotation movement. Moreover, it also realized training effect as the walking resist, by controlling a parameter of the controller. In this article, their effects were confirmed by theoretical analysis and experiments.

Dynamical system learning, also known as system identification, is a powerful tool for modeling unknown systems from data. However, guaranteeing properties such as stability of the obtained model is generally difficult, especially for complex nonlinear models. Therefore, for industrial applications, a reliable learning method that can ensure that the model has some desirable properties is required. In this paper, a novel method for learning a nonlinear model with guaranteed topological properties is proposed. The method employs a model structure represented by transforming the internal state, defined by an internal model with simple known dynamic properties, by a homeomorphic map. This enables to learn a model that guarantees not only the stability of the model but also the existence of limit cycles and other topological properties of the state trajectories in state space.