Quantum communication is a promising technique for enhancing the information security. However, the limited availability of quantum keys due to low generation rates still remains a challenge in the quantum key distribution of power systems. To address this issue and optimize the utilization of quantum resources in the gradually developed quantum-secured power communication systems, this article proposes a hybrid resource optimization method by leveraging network virtualization technologies. First, we present a virtualized architecture using software-defined networking and network function virtualization to support multiapplication scenarios in power system communications. Subsequently, a two-stage resource optimization strategy is proposed to manage both classical and quantum resources. At the first stage, we design a network slicing scheme for allocating classical communication resources, such as channel capacity and time delay. This scheme aims to segregate information flows from applications with diversified quality of service requirements. At the second stage, encryption resources are allocated by matching limited quantum keys with communication requests with different priorities, thereby improving the overall performance of quantum-classical power communication systems. Finally, we construct a cosimulation testbed to validate the effectiveness of the proposed method through case studies.

The problem of tracking a Cartesian space trajectory for an object held by two redundant robots while controlling the contact force and the object's internal force is investigated in this paper. A two-level impedance control (TLIC) algorithm incorporating an error reference controller (ERC) is developed. This algorithm is robust to system and environmental kinematic and dynamic uncertainties. Joint redundancy is used to fulfil additional tasks such as singularity robustness. The control algorithm is applicable to dual independent as well as closed-chain robot control. The algorithm has been extensively tested using a computer simulation of the full dynamic model of a dual-arm experimental redundant system in our Robotics and Control Systems Laboratory. The algorithm has also been implemented on the dual-arm experimental system. Experimental results that illustrate various features of the dual-arm control algorithm are presented in this paper.