Multi-sensor information fusion predictive control algorithm

被引：1
作者：
Zhao M. [1 ]
Li Y. [1 ,2 ]
Hao G. [2 ]
机构：
[1] School of Computer and Information Engineering, Harbin University of Commerce, Harbin, 150001, HeiLongjiang
[2] Electronic Engineering Institute, Heilongjiang University, Harbin, 150080, Heilongjiang
来源：
International Journal of Multimedia and Ubiquitous Engineering | 2016年 / 11卷 / 04期
关键词：
Centralized fusion; Covariance intersection fusion; Information fusion; Matrices weighted; Predictive control;
D O I：
10.14257/ijmue.2016.11.4.06
中图分类号：
学科分类号：
摘要：
The multi-sensor information fusion predictive control algorithm for discrete-time linear time-invariant stochastic control system is presented in this paper. This algorithm combines the fusion steady-state Kalman filter with the predictive control. It avoids the complex Diophantine equation and it can obviously reduce the computational burden. The algorithm can deal with the multi-sensor discrete-time linear time-invariant stochastic controllable system based on the linear minimum variance optimal information fusion criterion. The fusion method includes the centralized fusion, matrices weighted and the covariance intersection fusion. Under the linear minimum variance optimal information fusion criterion, the calculation formula of optimal weighting coefficients have be given in order to realize matrices weighted. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection fusion algorithm, which can reduce the computational burden. And the relationship between the accuracy and the computation complexities among the three fusion algorithm are analyzed. Compared with the single sensor case, the accuracy of the fused filter is greatly improved. A simulation example of the target tracking controllable system with two sensors shows its effectiveness and correctness. © 2016 SERSC.
引用
收藏
页码：49 / 58
页数：9
相关论文
共 23 条
  • [1] Guodong S., Shouda J., Linalei L., Information Fusion Kalman Filter with Complex Coloured Noise for Descriptor Systems, 34, 5, pp. 1195-1200, (2013)
  • [2] Li D.Z., Lei G., Jian R.C., Measurement Fusion Steady-State Kalman Filtering Algorithm with Correlated Noises and Global Optimdity, 31, 3, pp. 556-560, (2009)
  • [3] Sun S.L., Ma J., Optimal filtering and smoothing for discrete-time stochastic singular systems, Signal Processing, 87, 1, pp. 189-201, (2007)
  • [4] Zeng W.G., Bi W.S., Wen X.B., Advanced process control, Beijing, Tsinghua University Press, pp. 35-69, (2002)
  • [5] An L.Q., Ping L., Constrained multivariable adaptive generalized predictive control for diagonal carima model, Control and Decision, 24, 3, pp. 300-334, (2009)
  • [6] Ding B.C., Xi Y., Cychowskic M., A synthesis approach for output feedback robust constrained model predictive control, Automatica, 44, pp. 258-264, (2008)
  • [7] Geng X.Y., Wei L.D., Fundamental philosophy and status of qualitative synthesis of model predictive control, Acta Automatica Sinica, 34, 10, pp. 1225-1234, (2008)
  • [8] Yuan N.X., Heng W., Compensator design and stability analysis for networked control systems, Control Theory & Applications, 25, 2, pp. 217-222, (2008)
  • [9] Wei L.D., Geng X.Y., Design and analysis of predictive networked control system, Control and Decision, 22, 9, pp. 1065-1069, (2007)
  • [10] Bin T., Yun Z., Ping L.G., Hua G.W., Networked generalized predictive control based on state-space model, Control and Decision, 25, 4, pp. 535-541, (2010)
123