A New Privacy Preservation Mechanism and a Gain Iterative Disturbance Observer for Multiagent Systems
Min Wang, Hongjing Liang, Yingnan Pan, Xiangpeng Xie
Abstract
A new privacy preservation mechanism and a gain iterative disturbance observer are proposed for multiagent systems. The proposed privacy protection mechanism can realize the privacy preservation in the user-defined time, which can improve the encryption capability and mitigate the impact on system performance. More specifically, a setting time mask function is constructed, and it is used as the transformed function so that the whole system is imposed with privacy protection rather than partial protection. Furthermore, a gain iterative disturbance observer is designed in this article, which can improve the control accuracy of the system. Based on the negative gradient optimization concept, a gain iteration mechanism is constructed instead of utilizing a constant as the gain of the disturbance function, which extends the performance to adjust system control accuracy on the basis of the original disturbance observer. Finally, the effectiveness of the proposed mechanism is illustrated by a simulation example.