Multisensor Information Fusion Predictive Control for time-varying systems

摘要：

Aiming at the multisensor discrete-time linear time-varying stochastic controllable system in the linear minimum variance optimal information fusion criterion, based on state space model, a multisensor information fusion weighted by scalars predictive control algorithm for time-varying systems is presented. This algorithm combines the fusion Kalman filter with predictive control, and it solves the control problem of time-varying systems, furthermore it avoids the complex Diophantine equation and it can obviously reduce the computational burden. Comparing to the single sensor case, the accuracy of the predictive control for time-varying systems is evidently improved. A simulation example of the target tracking controllable system with three sensors shows its effectiveness and correctness.

关键词： Time-Varying Systems Predictive Control Information Fusion State-space Model Weighted by Scalars

作者: Li Yun Zhao Ming Hao Gang Xing Zong-xin

作者单位: School of Computer and Information Engineering Harbin University of Commerce Harbin, China School of Computer and Information ngineering Harbin University of Commerce Harbin, China Electronic Engineering Institute Heilongjiang University Harbin, China Science and Technology Agency Heilongjiang University Harbin, China

会议类型: 国际会议

会议名称: 2012 International Conference on Measurement,Information and Control(2012测量、信息与控制国际会议 ICMIC2012)

会议地点: 哈尔滨

会议语种:英文

页码: 378-382

在线出版日期: 2012-05-18（万方平台首次上网日期，不代表论文的发表时间）