Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation

Yongfeng Lv; Jing Na; Qinmin Yang; Guido Herrmann

doi:10.1109/IJCNN.2016.7727650

Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation

Yongfeng Lv
, Jing Na
, Qinmin Yang
, Guido Herrmann

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

8 Citations (Scopus)

528 Downloads (Pure)

Abstract

In this paper, an alternative solution for adaptive optimal tracking control of nonlinear completely unknown systems is proposed. Firstly, an adaptive identifier is used to estimate the unknown system dynamics. Then, a recently developed system augmentation approach is adopted to design the optimal control, where the reference signal is incorporated into the augmented system. Thus, both the feedforward control and feedback control can be obtained simultaneously. Then, a critic neural network (NN) is used to estimate the augmented performance index, and calculate the optimal control action. Thus, the widely used actor NN is not needed. Finally, a new adaptive law recently proposed by the authors is used to online update the NN weight. The closed-loop stability and the convergence of the optimal control are all proved. The feasibility of the suggested approach is demonstrated by a simulation example.

Original language	English
Title of host publication	2016 International Joint Conference on Neural Networks (IJCNN 2016)
Subtitle of host publication	Proceedings of a meeting held 24-27 July 2016, Vancouver, Canada
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3516-3521
Number of pages	6
ISBN (Electronic)	9781509006205
ISBN (Print)	9781509006212
DOIs	https://doi.org/10.1109/IJCNN.2016.7727650
Publication status	Published - Feb 2017
Event	2016 IEEE International Joint Conference on Neural Networks (IJCNN) - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks (IJCNN)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)	2161-4407

Conference

Conference	2016 IEEE International Joint Conference on Neural Networks (IJCNN)
Country/Territory	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

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10.1109/IJCNN.2016.7727650

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Lv, Y., Na, J., Yang, Q., & Herrmann, G. (2017). Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation. In 2016 International Joint Conference on Neural Networks (IJCNN 2016): Proceedings of a meeting held 24-27 July 2016, Vancouver, Canada (pp. 3516-3521). (Proceedings of the International Joint Conference on Neural Networks (IJCNN)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IJCNN.2016.7727650

Lv, Yongfeng ; Na, Jing ; Yang, Qinmin et al. / Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation. 2016 International Joint Conference on Neural Networks (IJCNN 2016): Proceedings of a meeting held 24-27 July 2016, Vancouver, Canada. Institute of Electrical and Electronics Engineers (IEEE), 2017. pp. 3516-3521 (Proceedings of the International Joint Conference on Neural Networks (IJCNN)).

@inproceedings{da4386d153c640ad8b30dc0c66cba8c9,

title = "Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation",

abstract = "In this paper, an alternative solution for adaptive optimal tracking control of nonlinear completely unknown systems is proposed. Firstly, an adaptive identifier is used to estimate the unknown system dynamics. Then, a recently developed system augmentation approach is adopted to design the optimal control, where the reference signal is incorporated into the augmented system. Thus, both the feedforward control and feedback control can be obtained simultaneously. Then, a critic neural network (NN) is used to estimate the augmented performance index, and calculate the optimal control action. Thus, the widely used actor NN is not needed. Finally, a new adaptive law recently proposed by the authors is used to online update the NN weight. The closed-loop stability and the convergence of the optimal control are all proved. The feasibility of the suggested approach is demonstrated by a simulation example. ",

author = "Yongfeng Lv and Jing Na and Qinmin Yang and Guido Herrmann",

year = "2017",

month = feb,

doi = "10.1109/IJCNN.2016.7727650",

language = "English",

isbn = "9781509006212",

series = "Proceedings of the International Joint Conference on Neural Networks (IJCNN)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "3516--3521",

booktitle = "2016 International Joint Conference on Neural Networks (IJCNN 2016)",

address = "United States",

note = "2016 IEEE International Joint Conference on Neural Networks (IJCNN) ; Conference date: 24-07-2016 Through 29-07-2016",

}

Lv, Y, Na, J, Yang, Q & Herrmann, G 2017, Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation. in 2016 International Joint Conference on Neural Networks (IJCNN 2016): Proceedings of a meeting held 24-27 July 2016, Vancouver, Canada. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Institute of Electrical and Electronics Engineers (IEEE), pp. 3516-3521, 2016 IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, 24/07/16. https://doi.org/10.1109/IJCNN.2016.7727650

Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation. / Lv, Yongfeng; Na, Jing; Yang, Qinmin et al.
2016 International Joint Conference on Neural Networks (IJCNN 2016): Proceedings of a meeting held 24-27 July 2016, Vancouver, Canada. Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 3516-3521 (Proceedings of the International Joint Conference on Neural Networks (IJCNN)).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation

AU - Lv, Yongfeng

AU - Na, Jing

AU - Yang, Qinmin

AU - Herrmann, Guido

PY - 2017/2

Y1 - 2017/2

N2 - In this paper, an alternative solution for adaptive optimal tracking control of nonlinear completely unknown systems is proposed. Firstly, an adaptive identifier is used to estimate the unknown system dynamics. Then, a recently developed system augmentation approach is adopted to design the optimal control, where the reference signal is incorporated into the augmented system. Thus, both the feedforward control and feedback control can be obtained simultaneously. Then, a critic neural network (NN) is used to estimate the augmented performance index, and calculate the optimal control action. Thus, the widely used actor NN is not needed. Finally, a new adaptive law recently proposed by the authors is used to online update the NN weight. The closed-loop stability and the convergence of the optimal control are all proved. The feasibility of the suggested approach is demonstrated by a simulation example.

AB - In this paper, an alternative solution for adaptive optimal tracking control of nonlinear completely unknown systems is proposed. Firstly, an adaptive identifier is used to estimate the unknown system dynamics. Then, a recently developed system augmentation approach is adopted to design the optimal control, where the reference signal is incorporated into the augmented system. Thus, both the feedforward control and feedback control can be obtained simultaneously. Then, a critic neural network (NN) is used to estimate the augmented performance index, and calculate the optimal control action. Thus, the widely used actor NN is not needed. Finally, a new adaptive law recently proposed by the authors is used to online update the NN weight. The closed-loop stability and the convergence of the optimal control are all proved. The feasibility of the suggested approach is demonstrated by a simulation example.

U2 - 10.1109/IJCNN.2016.7727650

DO - 10.1109/IJCNN.2016.7727650

M3 - Conference Contribution (Conference Proceeding)

SN - 9781509006212

T3 - Proceedings of the International Joint Conference on Neural Networks (IJCNN)

SP - 3516

EP - 3521

BT - 2016 International Joint Conference on Neural Networks (IJCNN 2016)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2016 IEEE International Joint Conference on Neural Networks (IJCNN)

Y2 - 24 July 2016 through 29 July 2016

ER -

Lv Y, Na J, Yang Q, Herrmann G. Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation. In 2016 International Joint Conference on Neural Networks (IJCNN 2016): Proceedings of a meeting held 24-27 July 2016, Vancouver, Canada. Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 3516-3521. (Proceedings of the International Joint Conference on Neural Networks (IJCNN)). Epub 2016 Nov 3. doi: 10.1109/IJCNN.2016.7727650

Adaptive Optimal Tracking Control of Unknown Nonlinear Systems Using System Augmentation

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