Adaptive compliance shaping with human impedance estimation

Robotics has been a promising and popular research area for the past few decades. Among various applications of robotic, in many cases, human are involved in different manners. Therefore, as an important sub research area of robotics, human robot interaction has drawn decent attention recently. It has been deeply and widely studied. For human robot interaction, human play an important role. Undoubtedly, the more we know about human, the easier we can do human robot interaction and the better performance we can achieve in human robot interaction. One fascinating research topic of human robot interaction would be human in exoskeleton, where human play a key role in the mechanical design of exoskeleton as well as the control strategy design of exoskeleton. Among all those applications, the augmentation exoskeleton is especially interesting due to its ability to amplify human. As mentioned previously, human properties are important for the design of exoskeleton. Unfortunately, despite many inspiring and deep studies about human properties and various proposed human models, human remains to be a complicated system that is hard to predict and model. Furthermore, human is a dynamic system whose parameters keep changing with time, bringing more challenges. As we all know, limited understanding of the control plant will limit the performance of the controller and bring difficulties in the design of a controller. In fact, the performance of many existed controller for augmentation exoskeleton is limited by using conservative values of human property parameters. A straightforward way to solve this problem is to estimate human properties online. Under this circumstance, the main challenges are to develop a control strategy, whose performance can be exploited using the estimation of human properties and a reliable method to online estimate human properties. This thesis mainly presents an adaptive compliance shaping control strategy with human impedance estimation and a brief review of a newly proposed complex stiffness model of human.