A minimized zero mean entropy approach to networked control systems

Jianhua Zhang; Hong Wang

doi:10.1109/CDC.2009.5400679

A minimized zero mean entropy approach to networked control systems

Jianhua Zhang, Hong Wang

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

Abstract

A novel control method is proposed for networked control systems with nonlinear process, probably non-Gaussian process noise and time delays. The performance index of closed loop control system consists of entropy, mean value and control energy constraint. Two stochastic control methods for networked control systems are given under the same general frame. One method utilizes gradient optimal techniques to obtain an optimal control law, and the stability of closed loop systems is analyzed. The other method gives the optimal solution of control law directly. The implementation issues are emphasized. The sliding window techniques and non-parametric probability density function estimation techniques are employed to obtain the entropy of tracking error recursively. As a result, the controller can be on-line tuned. Finally, the methodology is illustrated by simulations. ©2009 IEEE.

Original language	English
Title of host publication	Proceedings of the IEEE Conference on Decision and Control\|Proc IEEE Conf Decis Control
Pages	6876-6881
Number of pages	5
DOIs	https://doi.org/10.1109/CDC.2009.5400679
Publication status	Published - 2009
Event	48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 - Shanghai Duration: 1 Jul 2009 → …

Conference

Conference	48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
City	Shanghai
Period	1/07/09 → …

Access to Document

10.1109/CDC.2009.5400679

Cite this

@inproceedings{b8c2b8c398ab42ccb74ea3a3aad0c7db,

title = "A minimized zero mean entropy approach to networked control systems",

abstract = "A novel control method is proposed for networked control systems with nonlinear process, probably non-Gaussian process noise and time delays. The performance index of closed loop control system consists of entropy, mean value and control energy constraint. Two stochastic control methods for networked control systems are given under the same general frame. One method utilizes gradient optimal techniques to obtain an optimal control law, and the stability of closed loop systems is analyzed. The other method gives the optimal solution of control law directly. The implementation issues are emphasized. The sliding window techniques and non-parametric probability density function estimation techniques are employed to obtain the entropy of tracking error recursively. As a result, the controller can be on-line tuned. Finally, the methodology is illustrated by simulations. {\textcopyright}2009 IEEE.",

author = "Jianhua Zhang and Hong Wang",

year = "2009",

doi = "10.1109/CDC.2009.5400679",

language = "English",

isbn = "9781424438716",

pages = "6876--6881",

booktitle = "Proceedings of the IEEE Conference on Decision and Control|Proc IEEE Conf Decis Control",

note = "48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 ; Conference date: 01-07-2009",

}

Zhang, J & Wang, H 2009, A minimized zero mean entropy approach to networked control systems. in Proceedings of the IEEE Conference on Decision and Control|Proc IEEE Conf Decis Control. pp. 6876-6881, 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009, Shanghai, 1/07/09. https://doi.org/10.1109/CDC.2009.5400679

TY - GEN

T1 - A minimized zero mean entropy approach to networked control systems

AU - Zhang, Jianhua

AU - Wang, Hong

PY - 2009

Y1 - 2009

N2 - A novel control method is proposed for networked control systems with nonlinear process, probably non-Gaussian process noise and time delays. The performance index of closed loop control system consists of entropy, mean value and control energy constraint. Two stochastic control methods for networked control systems are given under the same general frame. One method utilizes gradient optimal techniques to obtain an optimal control law, and the stability of closed loop systems is analyzed. The other method gives the optimal solution of control law directly. The implementation issues are emphasized. The sliding window techniques and non-parametric probability density function estimation techniques are employed to obtain the entropy of tracking error recursively. As a result, the controller can be on-line tuned. Finally, the methodology is illustrated by simulations. ©2009 IEEE.

AB - A novel control method is proposed for networked control systems with nonlinear process, probably non-Gaussian process noise and time delays. The performance index of closed loop control system consists of entropy, mean value and control energy constraint. Two stochastic control methods for networked control systems are given under the same general frame. One method utilizes gradient optimal techniques to obtain an optimal control law, and the stability of closed loop systems is analyzed. The other method gives the optimal solution of control law directly. The implementation issues are emphasized. The sliding window techniques and non-parametric probability density function estimation techniques are employed to obtain the entropy of tracking error recursively. As a result, the controller can be on-line tuned. Finally, the methodology is illustrated by simulations. ©2009 IEEE.

U2 - 10.1109/CDC.2009.5400679

DO - 10.1109/CDC.2009.5400679

M3 - Conference contribution

SN - 9781424438716

SP - 6876

EP - 6881

BT - Proceedings of the IEEE Conference on Decision and Control|Proc IEEE Conf Decis Control

T2 - 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009

Y2 - 1 July 2009

ER -

A minimized zero mean entropy approach to networked control systems

Abstract

Conference

Access to Document

Fingerprint

Cite this