The advancement and development of human modeling have greatly benefited from principles used in robotics, for instance, multibody dynamics laid the foundations for physics engines of human movement simulation, and the robotics and control theory were used to contextualize human sensorimotor control. There are many common interests and interconnections between the fields of human modeling and robotics. In recent years, as robots have become safer and smarter, they actively participate in our lives and help us in various scenarios. Roboticists need tools and data from human modeling to build next-generation robots that better assist humans. In this survey, we focus on the connections between physical human-robot interaction and human modeling. On one hand, human neuromusculoskeletal and sensorimotor control models provide novel insights into the human response that robots can utilize to improve human performance. On the other hand, robots are becoming instrumental in quantifying the performance of the (neuro)musculoskeletal system. Thus, the combined use of human modeling and robotic methods in physical human-robot interaction can lead to both improved human understanding and functional assistance.

Supplementary control (SC) technology is widely leveraged by power supply companies in active distribution networks (ADNs) to improve their stability and dynamic characteristics. Yet, the existed SCs are generally implemented from inside the converter controllers of distributed generators (DGs) or active loads, so there is a need to redesign the internal physical structure of the existing controller, resulting in the increasing work amount of assembling and workability. This paper studies the specific R & D process of a novel external coupling type SC (ECSC), which is based upon current source injection (CSI-ECSC) for improving the dynamic characteristics of ADN. The SC current signals are coupled to the current sampling loop from outside the converter controller. And the employment of the existing current sample makes it unnecessary to redesign the internal physical structure of the existing controller. As a result, the SC assembling is simplified and its workability is improved. In this paper, a detailed exemplary ADN with direct-drive permanent magnet synchronous generator (PMSG) is firstly set up in math for full eigenvalue analysis. Then, the CSI-ECSC is designed with its control loop, interface circuit, and parameter setting. Furthermore, by using PSCAD/EMTDC, groups of case studies are conducted in ADNs where photovoltaics (PVs) and energy storage (ES) are included. Finally, the real-time hardware-in-the-loop (HIL) testing validates the functionality of the realized CSI-ECSC in RTDS.

In this paper, we propose a method for improving the human operator's arm posture during bilateral teleoperation. The method is based on a musculoskeletal model that considers human operator's arm dynamics and the feedback force from the haptic interface (master), which is used to control a robotic arm (slave) in a remote environment. We perform an online optimisation to find the optimal configuration that has the longest endurance time with respect to muscle fatigue. Next, a trajectory is generated on the haptic interface in order to guide the human arm into the optimal configuration. The teleoperation is temporarily suspended by decoupling the master from the slave robot when the haptic device is being reconfigured. Afterwards, the loop is coupled again and the slave robot is controlled from the position where it stopped after the haptic interface guided the operator's arm to the optimised configuration. The main advantage of the proposed method is that the human operator can perform the task with less effort, which increases the endurance time. To validate our approach, we performed proof-of-concept experiments on a teleoperation system composed of two Franka Emika robots, where one was serving as master and the other as slave.

In this paper, we propose a method for selective monitoring and management of human muscle fatigue in human-robot co-manipulation scenarios. The proposed approach uses a machine learning technique to learn the complex relationship between individual human muscle forces, arm configuration and arm endpoint force that are provided by a sophisticated offline musculoskeletal model. The estimated muscle forces are used in the fatigue model to estimate the individual muscle fatigue levels online. Two fatigue management protocols are proposed that enable the robot to handle and reduce the human fatigue by altering the configuration of task execution. The first protocol uses optimisation technique to find the optimal position for task execution, where the fatigue-related endurance time can be maximised. The second protocol divides the arm muscles into groups and then alters the direction of endpoint force so that the fatigued muscle group can relax and the relaxed muscle group becomes active. The proposed method has a potential to enable the robot to facilitate safer and more ergonomic working conditions for the human coworker. The main advantage of this approach is that it can operate online, and that all the measurements can be performed by the robot sensory system, which can significantly increase the applicability in real world scenarios. To validate the proposed method, we performed multiple experiments with two collaborative tasks (polishing and drilling) under different conditions.