An evolution model of data fusion system based on evolution procedure of nervous system is proposed. There are lots of similar characteristics between the evolution of nervous system and the development of data fusion system. It is reasonable to try to find guidelines in the theory of nervous system. For example, the function and structure of data fusion nodes almost take a same role as neurons in nervous system do, so we name the data fusion nodes as data fusion units. Just as the nervous system, the basic evolution architectures of data fusion system include four phases: Chaos (Autonomous), Fully Distributed, Centralized, and Internal Model Based Hierarchical. In the last phase of evolution, interface among unites become independent intelligent parts step by step. It provides a more flexible hierarchical data fusion architecture, which makes it be possible to simulate the regulation and adaptation mechanism of nervous system. The application analyses of these mechanisms to the data fusion systems proved that this dynamic hierarchy architecture is capable of deciding not only what to fuse and how to fuse but also when to fuse.
At low speed or zero crossings, the existed nonlinear effects such as friction, the fluctuation of the motor moment will make the system jitter heavily. Our researches focus on the analysis and compensation of the motor moment fluctuation when the optoelectronic tracking system works at low speed. Actually, other nonlinear factors at low speed are also checked. As the fluctuation is always presented in sine orderliness, a repeat controller that bases on the study law is applied to the tracking system. Both simulations and experiments show that with the repeat controller, the tracking precision increase about 3 times comparing to the PID one.