Advertisement

Soft Computing

, Volume 22, Issue 2, pp 487–497 | Cite as

Adaptive iterative learning control based on IF–THEN rules and data-driven scheme for a class of nonlinear discrete-time systems

  • Chidentree Treesatayapun
Methodologies and Application

Abstract

An adaptive iterative learning controller (ILC) is designed for a class of nonlinear discrete-time systems based on data driving control (DDC) scheme and adaptive networks called fuzzy rules emulated network (FREN). The proposed control law is derived by using DDC scheme with a compact form dynamic linearization for iterative systems. The pseudo-partial derivative of linearization model is estimated by the proposed tuning algorithm and FREN established by human knowledge of controlled plants within the format of IF–THEN rules related on input–output data set. An on-line learning algorithm is proposed to compensate unknown nonlinear terms of controlled plant, and the controller allows to change desired trajectories for other iterations. The performance of control scheme is verified by theoretical analysis under reasonable assumptions which can be held for a general class of practical controlled plants. The experimental system is constructed by a commercial DC motor current control to confirm the effectiveness and applicability. The comparison results are addressed with a general ILC scheme based on DDC.

Keywords

Iterative learning control Data-driven control Discrete-time systems Adaptive control DC motor Neuro-fuzzy 

Notes

Acknowledgments

The author gratefully acknowledges the contributions of CINVESTAV-IPN’s research Grant 2013–2014 and Mexican Research Organization CONACyT Grant # 257253.

Compliance with ethical standards

Conflict of interest

Chidentree Treesatayapun declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. Arimoto S, Kawamura S, Miyazaki F (1984) Bettering operation of robots by learning. J Robot Syst 1(2):123–140CrossRefGoogle Scholar
  2. Chi R, Hou Z, Huang B, Jin S (2014) A unified data-driven design framework of optimality-based generalized iterative learning control. Comput Chem Eng 77:10–23CrossRefGoogle Scholar
  3. Chi R, Hou Z, Jin S (2015) A data-driven adaptive ILC for a class of nonlinear discrete-time systems with random initial states and iteration-varying target trajectory. J Franklin Inst 352:2407–2424MathSciNetCrossRefGoogle Scholar
  4. Chi R, Liu Y, Hou Z, Jin S (2015) Data-driven terminal iterative learning control with high-order learning law for a class of non-linear discrete-time multiple-inputmultiple output systems. IET Control Theory Appl 9:1075–1082MathSciNetCrossRefGoogle Scholar
  5. Chien CJ (2008) A combined adaptive law for fuzzy iterative learning control of nonlinear systems with varying control tasks. IEEE Trans Fuzzy Syst 16(1):40–51MathSciNetCrossRefGoogle Scholar
  6. Chien CJ, Fu LC (2002) An iterative learning control of nonlinear systems using neural network design. Asian J Control 4(1):21–29MathSciNetCrossRefGoogle Scholar
  7. Chi R, Hou Z (2007) Dual-stage optimal iterative learning control for nonlinear non-affine discrete-time systems. Acta Automat Sinica 33(10):1061–1065MathSciNetCrossRefMATHGoogle Scholar
  8. Choi JY, Lee JS (2000) Adaptive iterative learning control of uncertain robotic systems. IEE Proc Control Theory Appl 147(2):217–223CrossRefGoogle Scholar
  9. Fang X, Zheng D, He H, Ni Z (2015) Data-driven heuristic dynamic programming with virtual reality. Neurocomputing 166:244–255CrossRefGoogle Scholar
  10. Han J, Shen D, Chien CJ (2015) Terminal iterative learning control for discrete-time nonlinear system based on neural networks. In: Proceedings of the 34th Chinese control conference, Hangzhou, China, pp 3190–3195Google Scholar
  11. Helfrich BE, Lee C, Bristow DA, Xiao XH, Dong J, Alleyne AG, Salapaka SM, Ferreira PM (2010) Combined \(H_{\infty }\)-feedback control and iterative learning control design with application to nanopositioning systems. IEEE Trans Control Syst Technol 18(2):336–351CrossRefGoogle Scholar
  12. Hou ZS, Jin ST (2011) A novel data-driven control approach for a class of discrete-time nonlinear systems. IEEE Trans Control Syst Technol 19(6):1549–1558CrossRefGoogle Scholar
  13. Hou ZS, Wang Z (2013) From model-based control to data-driven control: survey, classification and perspective. Inf Sci 235:3–35MathSciNetCrossRefMATHGoogle Scholar
  14. Kemal U, Gulay OG (2016) An adaptive support vector regressor controller for nonlinear systems. Soft Comput 20:2531–2556CrossRefMATHGoogle Scholar
  15. Tayebi A (2004) Adaptive iterative learning control for robot manipulators. Automatica 40(7):1195–1203MathSciNetCrossRefMATHGoogle Scholar
  16. Treesatayapun C (2015) A data-driven adaptive controller for a class of unknown nonlinear discrete-time systems with estimated PPD. Eng Sci Technol Int J 18:218–228Google Scholar
  17. Treesatayapun C (2015) Data input-output adaptive controller based on IF-THEN rules for a class of non-affine discrete-time systems: the robotic plant. J Intell Fuzzy Syst 28:661–668MathSciNetGoogle Scholar
  18. Treesatayapun C, Uatrongjit S (2005) Adaptive controller with fuzzy rules emulated structure and its applications. Eng Appl Artificial Intell 18:603–615Google Scholar
  19. Treesatayapun C, Uatrongjit S (2006) Controlling chaos by hybrid system based on FREN and sliding mode control. ASME J Dyn Syst Measurment Contr 128(2):352–358CrossRefGoogle Scholar
  20. Waldock A, Carse B (2016) Learning a robot controller using an adaptive hierarchical fuzzy rule-based system. Soft Comput 20:2855–2881CrossRefGoogle Scholar
  21. Wang HR, Yang L, Wei LX (2007) Fuzzy-neuro position/force control for robotic manipulators with uncertainties. Soft Comput 11:311–315CrossRefMATHGoogle Scholar
  22. Wang D, Liu D, Li H (2016) A neural-network-based online optimal control approach for nonlinear robust decentralized stabilization. Soft Comput 20:707–716CrossRefMATHGoogle Scholar
  23. Wang YC, Chien CJ (2013) Design and analysis of fuzzy-neural discrete adaptive iterative learning control for nonlinear plants. Int J Fuzzy Syst 15(2):149–158MathSciNetGoogle Scholar
  24. Zhu Y, Hou ZS (2014) Data-driven MFAC for a class of discrete-time nonlinear systems with RBFNN. IEEE Trans Neural Netw Learn Syst 25(5):1013–2014CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Robotic and Advanced ManufacturingCINVESTAV-SaltilloRamos ArizpeMexico

Personalised recommendations