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Sequential fusion estimation for multisensor systems with non-gaussian noises

Science China(Information Sciences) 2020年第12期63卷 153-165页

作者： Liping YAN Chenying DI Q.M.Jonathan WU Yuanqing XIA Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Department of Electrical and Computer Engineering University of Windsor

The sequential fusion estimation for multisensor systems disturbed by non-gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-gaussian noises.

关键词： state estimation sequential fusion non-gaussian disturbance heavy-tailed noise multivariate $t$-distribution

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Performance Assessment of Multivariable Model Predictive Control Subjected to non-gaussian disturbances

Performance Assessment of Multivariable Model Predictive Con...

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第32届中国过程控制会议(CPCC2021)

作者： HAN Xin WANG Yu-hong College of Control Science and Engineering China University of Petroleum

Model predictive control(MPC) is suitable for dealing with multivariable optimization control problems with complex *** order to maintain control loops working with good performance,it is necessary to assess control performance of predictive *** conventional performance assessment index based on minimum variance(MV) is only suitable for system affected by gaussian *** this paper,a performance assessment index based on rational entropy(RE) is proposed to assess the performance of multivariable model predictive control subjected to non-gaussian *** MPC controller performance assessment has been carried out for the Wood-Berry distillation column with two inputs and two outputs under non-gaussian *** comparing with the traditional multivariate performance assessment index based on MV,the proposed performance assessment index based on minimum rational entropy is more effective.

关键词： Control loop Performance Assessment non-gaussian disturbance Rational Entropy Model Predictive Control Multivariable Control System

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