EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems

Zhou, Yuyang; Zhang, Qichun; Wang, Hong; Zhou, Ping; Chai, Tianyou

Abstract

In this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of nonlinear 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 a 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 analyzed in the mean-square sense. The simulation results are given to illustrate the effectiveness of the proposed control algorithm.

Citation

Zhou, Y., Zhang, Q., Wang, H., Zhou, P., & Chai, T. (2018). EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems. IEEE Transactions on Automatic Control, 63(4), 1155-1162. https://doi.org/10.1109/tac.2017.2742661

• Journal Article Type: Article
• Acceptance Date: Aug 7, 2017
• Online Publication Date: Aug 21, 2017
• Publication Date: 2018-04
• Deposit Date: Nov 22, 2021
• Journal: IEEE Transactions on Automatic Control
• Print ISSN: 0018-9286
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 63
• Issue: 4
• Pages: 1155-1162
• DOI: https://doi.org/10.1109/tac.2017.2742661
• Keywords: Extended Kalman filter (EKF), minimum entropy criterion, non-Gaussian stochastic nonlinear systems, tracking performance enhancement
• Public URL: http://researchrepository.napier.ac.uk/Output/2817800