Time scale analysis and synthesis for Model Predictive Control under stochastic environments

Yan Zhang; D. Subbaram Naidu; Hoa M. Nguyen; Chenxiao Cai; Yun Zou

doi:10.1109/ISRCS.2014.6900085

Time scale analysis and synthesis for Model Predictive Control under stochastic environments

Yan Zhang
, D. Subbaram Naidu
, Hoa M. Nguyen
, Chenxiao Cai
, Yun Zou

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

This paper presents a method of time-scale analysis and synthesis for Model Predictive Control (MPC) under stochastic environment. A high-order plant is decoupled into slow and fast subsystems using time-scale method with high-order accuracy. Based on the two subsystems, Kalman filters and sub-controllers are designed separately for the subsystems. Then a composite model predictive controller is obtained. The method is illustrated by applying the proposed method to wind energy conversion system. The response of the output from the composite model predictive controller is compared to that of the original MPC showing the simplicity and reduction in computation effort of the proposed method for Model Predictive Control.

Original language	English (US)
Title of host publication	7th International Symposium on Resilient Control Systems, ISRCS 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479941872
DOIs	https://doi.org/10.1109/ISRCS.2014.6900085
State	Published - Sep 16 2014
Event	7th International Symposium on Resilient Control Systems, ISRCS 2014 - Denver, United States Duration: Aug 19 2014 → Aug 21 2014

Publication series

Name	7th International Symposium on Resilient Control Systems, ISRCS 2014

Other

Other	7th International Symposium on Resilient Control Systems, ISRCS 2014
Country/Territory	United States
City	Denver
Period	8/19/14 → 8/21/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

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This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.1109/ISRCS.2014.6900085

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Zhang, Y., Subbaram Naidu, D., Nguyen, H. M., Cai, C., & Zou, Y. (2014). Time scale analysis and synthesis for Model Predictive Control under stochastic environments. In 7th International Symposium on Resilient Control Systems, ISRCS 2014 Article 6900085 (7th International Symposium on Resilient Control Systems, ISRCS 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISRCS.2014.6900085

Time scale analysis and synthesis for Model Predictive Control under stochastic environments. / Zhang, Yan; Subbaram Naidu, D.; Nguyen, Hoa M. et al.
7th International Symposium on Resilient Control Systems, ISRCS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 6900085 (7th International Symposium on Resilient Control Systems, ISRCS 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, Y, Subbaram Naidu, D, Nguyen, HM, Cai, C & Zou, Y 2014, Time scale analysis and synthesis for Model Predictive Control under stochastic environments. in 7th International Symposium on Resilient Control Systems, ISRCS 2014., 6900085, 7th International Symposium on Resilient Control Systems, ISRCS 2014, Institute of Electrical and Electronics Engineers Inc., 7th International Symposium on Resilient Control Systems, ISRCS 2014, Denver, United States, 8/19/14. https://doi.org/10.1109/ISRCS.2014.6900085

Zhang Y, Subbaram Naidu D, Nguyen HM, Cai C, Zou Y. Time scale analysis and synthesis for Model Predictive Control under stochastic environments. In 7th International Symposium on Resilient Control Systems, ISRCS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 6900085. (7th International Symposium on Resilient Control Systems, ISRCS 2014). doi: 10.1109/ISRCS.2014.6900085

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abstract = "This paper presents a method of time-scale analysis and synthesis for Model Predictive Control (MPC) under stochastic environment. A high-order plant is decoupled into slow and fast subsystems using time-scale method with high-order accuracy. Based on the two subsystems, Kalman filters and sub-controllers are designed separately for the subsystems. Then a composite model predictive controller is obtained. The method is illustrated by applying the proposed method to wind energy conversion system. The response of the output from the composite model predictive controller is compared to that of the original MPC showing the simplicity and reduction in computation effort of the proposed method for Model Predictive Control.",

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N2 - This paper presents a method of time-scale analysis and synthesis for Model Predictive Control (MPC) under stochastic environment. A high-order plant is decoupled into slow and fast subsystems using time-scale method with high-order accuracy. Based on the two subsystems, Kalman filters and sub-controllers are designed separately for the subsystems. Then a composite model predictive controller is obtained. The method is illustrated by applying the proposed method to wind energy conversion system. The response of the output from the composite model predictive controller is compared to that of the original MPC showing the simplicity and reduction in computation effort of the proposed method for Model Predictive Control.

AB - This paper presents a method of time-scale analysis and synthesis for Model Predictive Control (MPC) under stochastic environment. A high-order plant is decoupled into slow and fast subsystems using time-scale method with high-order accuracy. Based on the two subsystems, Kalman filters and sub-controllers are designed separately for the subsystems. Then a composite model predictive controller is obtained. The method is illustrated by applying the proposed method to wind energy conversion system. The response of the output from the composite model predictive controller is compared to that of the original MPC showing the simplicity and reduction in computation effort of the proposed method for Model Predictive Control.

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Time scale analysis and synthesis for Model Predictive Control under stochastic environments

Abstract

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