Skip to main content
SpringerLink
Log in

Adaptation-Oriented Near-Optimal Control and Robust Synthesis of an Overhead Crane System

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10639))

Included in the following conference series:

Abstract

In this paper, we develop an adaptation-oriented approximate optimal control strategy and apply it to perform robust stabilization of an overhead crane system including complex nonlinearity. Via employing a novel updating rule to the adaptive critic structure, the near-optimal control law can be learnt based on the converged weight vector. By further considering the dynamical uncertainties, it is proven that the developed near-optimal control law can achieve uniform ultimate boundedness of the closed-loop state vector, thereby guaranteeing a certain extent of robustness for the uncertain nonlinear plant. An experimental simulation with respect to the overhead crane system is also conducted to verify the performance of the present control method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Werbos, P.J.: Approximate dynamic programming for real-time control and neural modeling. In: White, D.A., Sofge, D.A. (eds.) Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approach. Van Nostrand Reinhold, New York (1992)

    Google Scholar 

  2. Wang, D., Mu, C., Liu, D.: Data-driven nonlinear near-optimal regulation based on iterative neural dynamic programming. Acta Automatica Sinica 43, 366–375 (2017)

    Google Scholar 

  3. Liu, D., Wei, Q., Wang, D., Yang, X., Li, H.: Adaptive Dynamic Programming with Applications in Optimal Control. Springer, London (2017). doi:10.1007/978-3-319-50815-3

  4. Vamvoudakis, K.G., Modares, H., Kiumarsi, B., Lewis, F.L.: Game theory-based control system algorithms with real-time reinforcement learning: how to solve multiplayer games online. IEEE Control Syst. Mag. 37, 33–52 (2017)

    Article  MathSciNet  Google Scholar 

  5. Zhang, H., Zhang, X., Luo, Y., Yang, J.: An overview of research on adaptive dynamic programming. Acta Automatica Sinica 39, 303–311 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  6. Vamvoudakis, K.G., Lewis, F.L.: Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem. Automatica 46, 878–888 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  7. Mu, C., Wang, D.: Neural-network-based adaptive guaranteed cost control of nonlinear dynamical systems with matched uncertainties. Neurocomputing 245, 46–54 (2017)

    Article  Google Scholar 

  8. Zhang, H., Cui, L., Luo, Y.: Near-optimal control for nonzero-sum differential games of continuous-time nonlinear systems using single-network ADP. IEEE Trans. Cybern. 43, 206–216 (2013)

    Article  Google Scholar 

  9. Wang, D., Mu, C.: A novel neural optimal control framework with nonlinear dynamics: closed-loop stability and simulation verification. Neurocomputing 266, 353–360 (2017)

    Article  Google Scholar 

  10. Jiang, Y., Jiang, Z.P.: Robust adaptive dynamic programming and feedback stabilization of nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 25, 882–893 (2014)

    Article  Google Scholar 

  11. Zhang, Q., Zhao, D., Wang, D.: Event-based robust control for uncertain nonlinear systems using adaptive dynamic programming. In: IEEE Transactions on Neural Networks and Learning Systems (2017). in press

    Google Scholar 

  12. Wang, D., He, H., Liu, D.: Adaptive critic nonlinear robust control: a survey. IEEE Trans. Cybern. 47(10), 3429–3451 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by Beijing Natural Science Foundation under Grant 4162065, in part by the National Natural Science Foundation of China under Grants 61773373, U1501251, 61533017, and 61233001, and in part by the Early Career Development Award of SKLMCCS.

Author information

Authors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Ding Wang

Authors
  1. Ding Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ding Wang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wang, D. (2017). Adaptation-Oriented Near-Optimal Control and Robust Synthesis of an Overhead Crane System. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70136-3_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70135-6

  • Online ISBN: 978-3-319-70136-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation