Spontaneous emergence of leadership patterns drives synchronization in complex human networks

Synchronization of human networks is fundamental in many aspects of human endeavour. Recently, much research effort has been spent on analyzing how motor coordination emerges in human groups (from rocking chairs to violin players) and how it is affected by coupling structure and strength. Here we uncover the spontaneous emergence of leadership (based on physical signaling during group interaction) as a crucial factor steering the occurrence of synchronization in complex human networks where individuals perform a joint motor task. In two experiments engaging participants in an arm movement synchronization task, in the physical world as well as in the digital world, we found that specific patterns of leadership emerged and increased synchronization performance. Precisely, three patterns were found, involving a subtle interaction between phase of the motion and amount of influence. Such patterns were independent of the presence or absence of physical interaction, and persisted across manipulated spatial configurations. Our results shed light on the mechanisms that drive coordination and leadership in human groups, and are consequential for the design of interactions with artificial agents, avatars or robots, where social roles can be determinant for a successful interaction.

Development of a Robotic Catheter Manipulation System Based on BP Neural Network PID Controller

In the process of artificial interventional therapy, the operation of artificial catheter is not accurate, which will bring strong radiation damage to surgeons. The purpose of this study is to develop a catheter operating system of surgical robot to assist doctors in remote operation and avoid the influence of radiation. BP neural network plays an important role in the flexibility and rapidity of control. According to the actual output of the system, the control parameters of the controller are constantly adjusted to achieve better output effect. This paper introduces the practical application of BP neural network PID controller in the remote operation of the system and compares with the traditional PID controller. The results show that the new control algorithm is feasible and effective. The results show that the synchronization performance of BP neural network PID controller is better than that of traditional PID controller.

Input-constrained chaos synchronization of horizontal platform systems via a model predictive controller

Horizontal platform systems are mechanical systems that can exhibit both periodic and chaotic behaviour. Various control techniques have been developed to solve their chaos synchronization. However, the avoidance of control saturation is rarely considered in existing control techniques. In this paper, a model predictive controller is developed to address the input-constrained chaos synchronization of horizontal platform systems. To meet the requirements of computational efficiency, the highly efficient symplectic pseudospectral method is taken as the core solver of the model predictive controller. Two methods to impose the terminal boundary condition are considered. And two indices, i.e., the approximate synchronization time and the synchronization-consumed energy, are proposed to evaluate the synchronization performance. Numerical simulations demonstrate that the developed model predictive controller can achieve faster synchronization while owns better robustness under various kinds of external disturbances when compared to several feedback controllers.