Generalized predictive control of DEAP actuator based on RBF neural network

Zhaoguo Jiang; Qinglin Wang; Yuan Li

doi:10.1109/ASCC.2017.8287418

Generalized predictive control of DEAP actuator based on RBF neural network

Zhaoguo Jiang, Qinglin Wang, Yuan Li

School of Automation

Beijing Institute of Technology

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

1 Citation (Scopus)

Abstract

Dielectric Electro-active Polymer (DEAP) materials generally have strong hysteresis, nonlinearities, and uncertainties, these characteristics making the effective control of its actuators quite difficult. A generalized predictive control strategy based on RBF neural network was proposed. The nonlinear characteristics of a DEAP actuator are approximated by RBF neural network. On this basis, the generalized predictive control strategy is adopted to achieve the predictive control of DEAP actuator via rolling optimization and feedback adjustment. Simulation results indicate that the proposed method can weaken the interference caused by nonlinearities of DEAP materials, has adaptability, robustness compared with the traditional generalized predictive control approach.

Original language	English
Title of host publication	2017 Asian Control Conference, ASCC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1632-1637
Number of pages	6
ISBN (Electronic)	9781509015733
DOIs	https://doi.org/10.1109/ASCC.2017.8287418
Publication status	Published - 7 Feb 2018
Event	2017 11th Asian Control Conference, ASCC 2017 - Gold Coast, Australia Duration: 17 Dec 2017 → 20 Dec 2017

Publication series

Name	2017 Asian Control Conference, ASCC 2017
Volume	2018-January

Conference

Conference	2017 11th Asian Control Conference, ASCC 2017
Country/Territory	Australia
City	Gold Coast
Period	17/12/17 → 20/12/17

Access to Document

10.1109/ASCC.2017.8287418

Cite this

@inproceedings{2d6c0a84535e4009889b7d52617e5806,

title = "Generalized predictive control of DEAP actuator based on RBF neural network",

abstract = "Dielectric Electro-active Polymer (DEAP) materials generally have strong hysteresis, nonlinearities, and uncertainties, these characteristics making the effective control of its actuators quite difficult. A generalized predictive control strategy based on RBF neural network was proposed. The nonlinear characteristics of a DEAP actuator are approximated by RBF neural network. On this basis, the generalized predictive control strategy is adopted to achieve the predictive control of DEAP actuator via rolling optimization and feedback adjustment. Simulation results indicate that the proposed method can weaken the interference caused by nonlinearities of DEAP materials, has adaptability, robustness compared with the traditional generalized predictive control approach.",

author = "Zhaoguo Jiang and Qinglin Wang and Yuan Li",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 11th Asian Control Conference, ASCC 2017 ; Conference date: 17-12-2017 Through 20-12-2017",

year = "2018",

month = feb,

day = "7",

doi = "10.1109/ASCC.2017.8287418",

language = "English",

series = "2017 Asian Control Conference, ASCC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1632--1637",

booktitle = "2017 Asian Control Conference, ASCC 2017",

address = "United States",

}

Jiang, Z, Wang, Q & Li, Y 2018, Generalized predictive control of DEAP actuator based on RBF neural network. in 2017 Asian Control Conference, ASCC 2017. 2017 Asian Control Conference, ASCC 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1632-1637, 2017 11th Asian Control Conference, ASCC 2017, Gold Coast, Australia, 17/12/17. https://doi.org/10.1109/ASCC.2017.8287418

Generalized predictive control of DEAP actuator based on RBF neural network. / Jiang, Zhaoguo; Wang, Qinglin; Li, Yuan.
2017 Asian Control Conference, ASCC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1632-1637 (2017 Asian Control Conference, ASCC 2017; Vol. 2018-January).

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

TY - GEN

T1 - Generalized predictive control of DEAP actuator based on RBF neural network

AU - Jiang, Zhaoguo

AU - Wang, Qinglin

AU - Li, Yuan

PY - 2018/2/7

Y1 - 2018/2/7

N2 - Dielectric Electro-active Polymer (DEAP) materials generally have strong hysteresis, nonlinearities, and uncertainties, these characteristics making the effective control of its actuators quite difficult. A generalized predictive control strategy based on RBF neural network was proposed. The nonlinear characteristics of a DEAP actuator are approximated by RBF neural network. On this basis, the generalized predictive control strategy is adopted to achieve the predictive control of DEAP actuator via rolling optimization and feedback adjustment. Simulation results indicate that the proposed method can weaken the interference caused by nonlinearities of DEAP materials, has adaptability, robustness compared with the traditional generalized predictive control approach.

AB - Dielectric Electro-active Polymer (DEAP) materials generally have strong hysteresis, nonlinearities, and uncertainties, these characteristics making the effective control of its actuators quite difficult. A generalized predictive control strategy based on RBF neural network was proposed. The nonlinear characteristics of a DEAP actuator are approximated by RBF neural network. On this basis, the generalized predictive control strategy is adopted to achieve the predictive control of DEAP actuator via rolling optimization and feedback adjustment. Simulation results indicate that the proposed method can weaken the interference caused by nonlinearities of DEAP materials, has adaptability, robustness compared with the traditional generalized predictive control approach.

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U2 - 10.1109/ASCC.2017.8287418

DO - 10.1109/ASCC.2017.8287418

M3 - Conference contribution

AN - SCOPUS:85047476176

T3 - 2017 Asian Control Conference, ASCC 2017

SP - 1632

EP - 1637

BT - 2017 Asian Control Conference, ASCC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 11th Asian Control Conference, ASCC 2017

Y2 - 17 December 2017 through 20 December 2017

ER -

Generalized predictive control of DEAP actuator based on RBF neural network

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