An online single-network adaptive algorithm for continuous-time nonlinear optimal control

Jae Young Lee, Jin Bae Park, Yoon Ho Choi, Keun Uk Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose an online adaptive neural-algorithm to solve the CT nonlinear optimal control problems. Compared to the existing methods, which adopt the architecture with two neural networks (NNs) for actor-critic implementations, only one NN for critic is used to implement the algorithm, simplifying the structure of the computation model. Moreover, we also provide a generalized learning rule for updating the NN weights, which covers the existing critic update rules as special cases. The theoretical and numerical results are given under the required persistent excitation condition to verify and analyze stability and performance of the proposed method.

Original languageEnglish
Title of host publicationICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems
Pages1687-1690
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 13th International Conference on Control, Automation and Systems, ICCAS 2013 - Gwangju, Korea, Republic of
Duration: 2013 Oct 202013 Oct 23

Publication series

NameInternational Conference on Control, Automation and Systems
ISSN (Print)1598-7833

Other

Other2013 13th International Conference on Control, Automation and Systems, ICCAS 2013
CountryKorea, Republic of
CityGwangju
Period13/10/2013/10/23

Fingerprint

Adaptive algorithms
Neural networks

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Lee, J. Y., Park, J. B., Choi, Y. H., & Lee, K. U. (2013). An online single-network adaptive algorithm for continuous-time nonlinear optimal control. In ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems (pp. 1687-1690). [6704205] (International Conference on Control, Automation and Systems). https://doi.org/10.1109/ICCAS.2013.6704205
Lee, Jae Young ; Park, Jin Bae ; Choi, Yoon Ho ; Lee, Keun Uk. / An online single-network adaptive algorithm for continuous-time nonlinear optimal control. ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems. 2013. pp. 1687-1690 (International Conference on Control, Automation and Systems).
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title = "An online single-network adaptive algorithm for continuous-time nonlinear optimal control",
abstract = "In this paper, we propose an online adaptive neural-algorithm to solve the CT nonlinear optimal control problems. Compared to the existing methods, which adopt the architecture with two neural networks (NNs) for actor-critic implementations, only one NN for critic is used to implement the algorithm, simplifying the structure of the computation model. Moreover, we also provide a generalized learning rule for updating the NN weights, which covers the existing critic update rules as special cases. The theoretical and numerical results are given under the required persistent excitation condition to verify and analyze stability and performance of the proposed method.",
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Lee, JY, Park, JB, Choi, YH & Lee, KU 2013, An online single-network adaptive algorithm for continuous-time nonlinear optimal control. in ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems., 6704205, International Conference on Control, Automation and Systems, pp. 1687-1690, 2013 13th International Conference on Control, Automation and Systems, ICCAS 2013, Gwangju, Korea, Republic of, 13/10/20. https://doi.org/10.1109/ICCAS.2013.6704205

An online single-network adaptive algorithm for continuous-time nonlinear optimal control. / Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho; Lee, Keun Uk.

ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems. 2013. p. 1687-1690 6704205 (International Conference on Control, Automation and Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - In this paper, we propose an online adaptive neural-algorithm to solve the CT nonlinear optimal control problems. Compared to the existing methods, which adopt the architecture with two neural networks (NNs) for actor-critic implementations, only one NN for critic is used to implement the algorithm, simplifying the structure of the computation model. Moreover, we also provide a generalized learning rule for updating the NN weights, which covers the existing critic update rules as special cases. The theoretical and numerical results are given under the required persistent excitation condition to verify and analyze stability and performance of the proposed method.

AB - In this paper, we propose an online adaptive neural-algorithm to solve the CT nonlinear optimal control problems. Compared to the existing methods, which adopt the architecture with two neural networks (NNs) for actor-critic implementations, only one NN for critic is used to implement the algorithm, simplifying the structure of the computation model. Moreover, we also provide a generalized learning rule for updating the NN weights, which covers the existing critic update rules as special cases. The theoretical and numerical results are given under the required persistent excitation condition to verify and analyze stability and performance of the proposed method.

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Lee JY, Park JB, Choi YH, Lee KU. An online single-network adaptive algorithm for continuous-time nonlinear optimal control. In ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems. 2013. p. 1687-1690. 6704205. (International Conference on Control, Automation and Systems). https://doi.org/10.1109/ICCAS.2013.6704205