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EKF-based Enhanced Performance Controller Design for Non-linear Stochastic Systems

  • Yuyang Zhou
  • , Qichun Zhang
  • , Hong Wang
  • , Ping Zhou
  • , Tianyou Chai
    • Pacific Northwest National Laboratory
    • Northeastern University

    Research output: Contribution to journal › Article › peer-review

    Abstract

    In this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of non-linear and dynamic stochastic systems subjected to non- Gaussian noises. Although the existing standard PI controller can be used to obtain the basic tracking of the systems, the desired tracking performance of the stochastic systems is difficult to achieve due to the random noises. To improve the tracking performance, an enhanced performance loop is constructed using the EKF-based state estimates without changing the existing closed loop with PI controller. Meanwhile, the gain of the enhanced performance loop can be obtained based upon the entropy optimization of the tracking error. In addition, the stability of the closed loop system is analysed in the mean square sense. The simulation results are given to illustrate the effectiveness of the proposed control algorithm.

    Original languageEnglish
    JournalIEEE Transactions on Automatic Control
    Early online date21 Aug 2017
    DOIs
    Publication statusPublished - 2017

    Keywords

    • Algorithm design and analysis
    • Control systems
    • Entropy
    • extended Kalman filter
    • Kalman filters
    • minimum entropy criterion
    • Non-Gaussian stochastic non-linear systems
    • Probability density function
    • Stability analysis
    • Stochastic systems
    • tracking performance enhancement

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