[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Dynamically Weighted Model Predictive Control of Affine Nonlinear Systems Based on Two-Timescale Neurodynamic Optimization

  • Conference paper
  • First Online:
Advances in Neural Networks – ISNN 2020 (ISNN 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12557))

Included in the following conference series:

  • 1228 Accesses

Abstract

This paper discusses dynamically weighted model predictive control based on two-timescale neurodynamic optimization. Minimax optimization problems with dynamic weights in objective functions are used in the model predictive control. The minimax optimization problems are solved by using a two-timescale neurodynamic optimization approach. Examples on controlling HVAC (heating, ventilation, and air-conditioning) and CSTR (cooling continuous stirred tank reactor) systems are elaborated to substantiate the efficacy of the control approach.

This work was supported in part by the National Natural Science Foundation of China under Grant 61673330, by International Partnership Program of Chinese Academy of Sciences under Grant GJHZ1849, and the Research Grants Council of the Hong Kong Special Administrative Region of China, under Grants 11208517 and 11202318.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • 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

Similar content being viewed by others

References

  1. Aswani, A., Gonzalez, H., Sastry, S.S., Tomlin, C.: Provably safe and robust learning-based model predictive control. Automatica 49(5), 1216–1226 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  2. Che, H., Wang, J.: A collaborative neurodynamic approach to global and combinatorial optimization. Neural Netw. 114, 15–27 (2019)

    Article  MATH  Google Scholar 

  3. Ding, H., Wang, J.: Recurrent neural networks for minimum infinity-norm kinematic control of redundant manipulators. IEEE Trans. Syst. Man Cybern. - Part A 29(3), 269–276 (1999)

    Google Scholar 

  4. Dunbar, W.B., Murray, R.M.: Distributed receding horizon control for multi-vehicle formation stabilization. Automatica 42(4), 549–558 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  5. Guo, Z., Liu, Q., Wang, J.: A one-layer recurrent neural network for pseudoconvex optimization subject to linear equality constraints. IEEE Trans. Neural Netw. 22(12), 1892–1900 (2011)

    Article  Google Scholar 

  6. Han, M., Fan, J., Wang, J.: A dynamic feedforward neural network based on Gaussian particle swarm optimization and its application for predictive control. IEEE Trans. Neural Netw. 22(9), 1457–1468 (2011)

    Article  Google Scholar 

  7. Hu, X., Wang, J.: Design of general projection neural networks for solving monotone linear variational inequalities and linear and quadratic optimization problems. IEEE Trans. Syst. Man Cybern. - Part B: Cybern. 37(5), 1414–1421 (2007)

    Google Scholar 

  8. Hu, X., Wang, J.: An improved dual neural network for solving a class of quadratic programming problems and its k-winners-take-all application. IEEE Trans. Neural Netw. 19(12), 2022–2031 (2008)

    Article  Google Scholar 

  9. Le, X., Wang, J.: Robust pole assignment for synthesizing feedback control systems using recurrent neural networks. IEEE Trans. Neural Netw. Learn. Syst. 25(2), 383–393 (2014)

    Article  Google Scholar 

  10. Le, X., Wang, J.: Neurodynamics-based robust pole assignment for high-order descriptor systems. IEEE Trans. Neural Netw. Learn. Syst. 26(11), 2962–2971 (2015)

    Article  MathSciNet  Google Scholar 

  11. Le, X., Wang, J.: A two-time-scale neurodynamic approach to constrained minimax optimization. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 620–629 (2017)

    Article  MathSciNet  Google Scholar 

  12. Li, G., Yan, Z., Wang, J.: A one-layer recurrent neural network for constrained nonconvex optimization. Neural Netw. 61, 10–21 (2015)

    Article  MATH  Google Scholar 

  13. Liang, X., Wang, J.: A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints. IEEE Trans. Neural Netw. 11(6), 1251–1262 (2000)

    Article  Google Scholar 

  14. Liu, Q., Wang, J.: A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming. IEEE Trans. Neural Netw. 19(4), 558–570 (2008)

    Article  MathSciNet  Google Scholar 

  15. Liu, Q., Wang, J.: A one-layer recurrent neural network for constrained nonsmooth optimization. IEEE Trans. Syst. Man Cybern. - Part B: Cybern. 40(5), 1323–1333 (2011)

    Google Scholar 

  16. Liu, Q., Yang, S., Wang, J.: A collective neurodynamic approach to distributed constrained optimization. IEEE Trans. Neural Netw. Learn. Syst. 28(8), 1747–1758 (2017)

    Article  MathSciNet  Google Scholar 

  17. Liu, S., Wang, J.: A simplified dual neural network for quadratic programming with its KWTA application. IEEE Trans. Neural Netw. 17(6), 1500–1510 (2006)

    Article  Google Scholar 

  18. Mayne, D., Rawlings, J., Rao, C., Scokaert, P.: Constrained model predictive control: stability and optimality. Automatica 36(6), 789–814 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  19. Pan, Y., Wang, J.: Model predictive control for nonlinear affine systems based on the simplified dual neural network. In: Proceedings of IEEE International Symposium on Intelligent Control, pp. 683–688. IEEE (2009)

    Google Scholar 

  20. Pan, Y., Wang, J.: Model predictive control of unknown nonlinear dynamical systems based on recurrent neural networks. IEEE Trans. Ind. Electron. 59(8), 3089–3101 (2012)

    Article  MathSciNet  Google Scholar 

  21. Peng, Z., Wang, J., Han, Q.: Path-following control of autonomous underwater vehicles subject to velocity and input constraints via neurodynamic optimization. IEEE Trans. Ind. Electron. 66(11), 8724–8732 (2019)

    Article  Google Scholar 

  22. Peng, Z., Wang, D., Wang, J.: Predictor-based neural dynamic surface control for uncertain nonlinear systems in strict-feedback form. IEEE Trans. Neural Netw. Learn. Syst. 28(9), 2156–2167 (2017)

    MathSciNet  Google Scholar 

  23. Qin, S., Le, X., Wang, J.: A neurodynamic optimization approach to bilevel quadratic programming. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2580–2591 (2017)

    Article  MathSciNet  Google Scholar 

  24. Tang, W.S., Wang, J.: A recurrent neural network for minimum infinity-norm kinematic control of redundant manipulators with an improved problem formulation and reduced architectural complexity. IEEE Trans. Syst. Man Cybern. - Part B 31(1), 98–105 (2001)

    Google Scholar 

  25. Teeter, J., Chow, M.Y.: Application of functional link neural network to HVAC thermal dynamic system identification. IEEE Trans. Ind. Electron. 45(1), 170–176 (1998)

    Article  Google Scholar 

  26. Uppal, A., Ray, W., Poore, A.: On the dynamic behavior of continuous stirred tanks. Chem. Eng. Sci. 29, 957–985 (1974)

    Article  Google Scholar 

  27. Wang, J.: A deterministic annealing neural network for convex programming. Neural Netw. 7(4), 629–641 (1994)

    Article  MATH  Google Scholar 

  28. Wang, J.S., Wang, J., Gu, S.: Neurodynamics-based receding horizon control of an HVAC system. In: International Symposium on Neural Networks, vol. 2, pp. 120–128 (2019)

    Google Scholar 

  29. Xia, Y., Feng, G., Wang, J.: A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations. Neural Netw. 17(7), 1003–1015 (2004)

    Article  MATH  Google Scholar 

  30. Xia, Y., Feng, G., Wang, J.: A novel neural network for solving nonlinear optimization problems with inequality constraints. IEEE Trans. Neural Netw. 19(8), 1340–1353 (2008)

    Article  Google Scholar 

  31. Xia, Y., Leung, H., Wang, J.: A projection neural network and its application to constrained optimization problems. IEEE Trans. Circ. Syst. Part I 49(4), 447–458 (2002)

    Google Scholar 

  32. Xia, Y., Wang, J.: A general methodology for designing globally convergent optimization neural networks. IEEE Trans. Neural Netw. 9(6), 1331–1343 (1998)

    Article  Google Scholar 

  33. Xia, Y., Wang, J.: Global exponential stability of recurrent neural networks for solving optimization and related problems. IEEE Trans. Neural Netw. 11(4), 1017–1022 (2000)

    Article  Google Scholar 

  34. Xia, Y., Wang, J.: A general projection neural network for solving monotone variational inequalities and related optimization problems. IEEE Trans. Neural Netw. 15(2), 318–328 (2004)

    Article  Google Scholar 

  35. Xia, Y., Wang, J.: A recurrent neural network for nonlinear convex optimization subject to nonlinear inequality constraints. IEEE Trans. Circ. Syst. - Part I 51(7), 1385–1394 (2004)

    Google Scholar 

  36. Xia, Y., Wang, J., Fok, L.M.: Grasping force optimization of multi-fingered robotic hands using a recurrent neural network. IEEE Trans. Robot. Autom. 20(3), 549–554 (2004)

    Article  Google Scholar 

  37. Xia, Y., Wang, J.: A bi-projection neural network for solving constrained quadratic optimization problems. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 214–224 (2016)

    Article  MathSciNet  Google Scholar 

  38. Yan, Z., Le, X., Wang, J.: Tube-based robust model predictive control of nonlinear systems via collective neurodynamic optimization. IEEE Trans. Ind. Electron. 63(7), 4377–4386 (2016)

    Article  Google Scholar 

  39. Yan, Z., Wang, J.: Model predictive control of nonlinear affine systems based on the general projection neural network and its application to a continuous stirred tank reactor. In: Proceedings of International Conference on Information Science and Technology, pp. 1011–1015. IEEE (2011)

    Google Scholar 

  40. Yan, Z., Wang, J.: Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks. IEEE Trans. Ind. Inform. 8(4), 746–756 (2012)

    Article  Google Scholar 

  41. Yan, Z., Fan, J., Wang, J.: A collective neurodynamic approach to constrained global optimization. IEEE Trans. Neural Netw. Learn. Syst. 28(5), 1206–1215 (2017)

    Article  Google Scholar 

  42. Yan, Z., Wang, J.: A neurodynamic approach to bicriteria model predictive control of nonlinear affine systems based on a goal programming formulation. In: Proceedings of International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2012)

    Google Scholar 

  43. Yan, Z., Wang, J.: Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks. IEEE Trans. Neural Netw. Learn. Syst. 25(3), 457–469 (2014)

    Article  MathSciNet  Google Scholar 

  44. Yan, Z., Wang, J.: Nonlinear model predictive control based on collective neurodynamic optimization. IEEE Trans. Neural Netw. Learn. Syst. 26(4), 840–850 (2015)

    Article  MathSciNet  Google Scholar 

  45. Yan, Z., Wang, J., Li, G.: A collective neurodynamic optimization approach to bound-constrained nonconvex optimization. Neural Netw. 55, 20–29 (2014)

    Article  MATH  Google Scholar 

  46. Zhang, Y., Wang, J.: Recurrent neural networks for nonlinear output regulation. Automatica 37(8), 1161–1173 (2001)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Jiasen Wang & Jun Wang

  2. School of Data Science, City University of Hong Kong, Kowloon, Hong Kong

    Jun Wang

  3. Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China

    Jiasen Wang & Jun Wang

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

    Dongbin Zhao

Authors
  1. Jiasen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongbin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jiasen Wang or Jun Wang .

Editor information

Editors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

    Min Han

  2. School of Science, Harbin Institute of Technology, Weihai, China

    Sitian Qin

  3. School of Engineering and Applied Sciences, University of the District of Columbia, Washington, DC, USA

    Nian Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, J., Wang, J., Zhao, D. (2020). Dynamically Weighted Model Predictive Control of Affine Nonlinear Systems Based on Two-Timescale Neurodynamic Optimization. In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-64221-1_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64220-4

  • Online ISBN: 978-3-030-64221-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation