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Balancing control energy and tracking error for fuzzy rule emulated adaptive controller

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Abstract

In this article, an adaptive controller, which can minimize both tracking error and control energy, is introduced by fuzzy rule emulated network (FREN) for a class of non-affine discrete time systems. The controlled plant can be assumed as fully unknown system dynamic. Only the estimated boundary of pseudo partial derivative (PPD) is required for an on-line learning phase. The update law is derived to guarantee the convergence of tuned parameters. Lyapunov techniques are utilized to demonstrate the performance of a closed-loop system regarding the integration of the infinite cost function. The computer simulation and electronic circuit system validate the effectiveness of the proposed control scheme.

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References

  1. Hou ZS, Wang Z (2013) From model-based control to data-driven control: survey, classification and perspective. Inf Sci 235:3–35

    Article  MATH  MathSciNet  Google Scholar 

  2. Meng D, Jia Y, Du J, Yu F (2011) Data-driven control for relative degree systems via iterative learning. IEEE Trans Neural Netw 22(12):2213–2225

    Article  Google Scholar 

  3. Coelho LDS, Marcelo WP, Rodrigo RS, Coelho AAR (2010) Model-free adaptive control design using evolutionary-neural compensator. Expert Syst Appl 37(1):499–508

    Article  Google Scholar 

  4. Hahn B, Oldham KR (2012) A model-free ON-OFF iterative adaptive controller based on stochastic approximation. IEEE Trans Control Syst Technol 20(1):196–204

    Article  Google Scholar 

  5. 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):1549–1558

    Article  Google Scholar 

  6. Khan SG, Herrmann G, Lewis FL, Pipe T, Melhuish C (2012) Reinforcement learning and optimal adaptive control: an overview and implementation examples. Annu Rev Control 36:42–59

    Article  Google Scholar 

  7. Cheng ST, Shih JS, Chang TY (2013) GA-based actuator control method for minimizing power consumption in cyber physical systems. Appl Intell 38:78–87

    Article  Google Scholar 

  8. Wang L, Liu Z, Chen CL, Zhang Y (2013) Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking. Appl Intell. doi:10.1007/s10489-013-0472-2

    Google Scholar 

  9. Li P, Xiao H (2013) An improved quantum-behaved particle swarm optimization algorithm. Appl Intell. doi:10.1007/s10489-013-0477-x

    Google Scholar 

  10. Al-Tamimi A, Lewis FL, Abu-Khalaf M (2008) Discrete-time nonlinear HJB solution using approximate dynamic programming: convergence proof. IEEE Trans Syst Man Cybern B 38(4):943–949

    Article  Google Scholar 

  11. Liu D, Wei Q (2013) Finite-approximation-error-based optimal control approach for discrete-time nonlinear systems. IEEE Trans Cybern 43(2):779–789

    Article  Google Scholar 

  12. Zhang H, Wei Q, Liu D (2011) An iterative adaptive dynamic programming method for solving a class of nonlinear zero-sum differential games. Automatica 47(1):207–214

    Article  MATH  MathSciNet  Google Scholar 

  13. Li H, Liu D (2012) Optimal control for discrete-time affine non-linear systems using general value iteration. IET Control Theory Appl 6(18):2725–2736

    Article  MathSciNet  Google Scholar 

  14. Dierks T, Jagannathan S (2012) Online optimal control of affine nonlinear discrete-time systems with unknown internal dynamics by using time-based policy update. IEEE Trans Neural Netw 23(7):1118–1129

    Article  Google Scholar 

  15. Treesatayapun C, Uatrongjit S (2005) Adaptive controller with Fuzzy rules emulated structure and its applications’. Eng Appl Artif Intell 18:603–615. Elsevier

    Article  Google Scholar 

  16. Treesatayapun C (2009) Nonlinear discrete-time controller based on fuzzy-rule emulated network and shuttering condition. Appl Intell 31:292–304

    Article  Google Scholar 

  17. Treesatayapun C (2011) A discrete-time stable controller for an omni-directional mobile robot based on an approximated model. Control Eng Pract 19:194–230. Elsevier

    Article  Google Scholar 

  18. Yang C, Ge SS, Xiang C, Chai T, Lee TH (2008) Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach. IEEE Trans Neural Netw 19(11):1873–1886

    Article  Google Scholar 

  19. Liu YJ, Chen CLP, Wen GX, Tong S (2011) Adaptive neural output feedback tracking control for a class of uncertain discrete-time nonlinear systems. IEEE Trans Neural Netw 22(7):1162–1167

    Article  Google Scholar 

  20. Chi R, Wang D, Hou Z, Jin S (2012) Data-driven optimal terminal iterative learning control. J Process Control 22:2026–2037

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Robotic and Advanced Manufactory, CINVESTAV-Saltillo, Ramos Arizpe, 25900, Mexico

    Chidentree Treesatayapun

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  1. Chidentree Treesatayapun
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Correspondence to Chidentree Treesatayapun.

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Treesatayapun, C. Balancing control energy and tracking error for fuzzy rule emulated adaptive controller. Appl Intell 40, 639–648 (2014). https://doi.org/10.1007/s10489-013-0493-x

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