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Research Article

Output Feedback Controller for a Class of Unknown Nonlinear Discrete Time Systems Using Fuzzy Rules Emulated Networks and Reinforcement Learning

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References

  • Hou ZS, Wang Z. From model-based control to data-driven control: survey, classification and perspective. Inf Sci. 2013;235:3–35.
  • Zhu Y, Hou ZS. Data-driven MFAC for a class of discrete-time nonlinear systems with RBFNN. IEEE Trans Neural Netw Learn Syst. 2014;25(5):1013–1020.
  • Wang X, Li X, Wang J, et al. Data-driven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleton. Inf Sci. 2016;327:246–257.
  • Lin N, Chi R, Huang B. Data-driven recursive least squares methods for non-affined nonlinear discrete-time systems. Appl Math Modell. 2020;81:787–798.
  • Kaldmae A, Kotta U. Input–output linearization of discrete-time systems by dynamic output feedback. Eur J Control. 2014;20:73–78.
  • Treesatayapun C. 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. 2015;28:661–668.
  • Liu YJ, Tong S. Adaptive NN tracking control of uncertain nonlinear discrete-time systems with nonaffine dead-zone input. IEEE Trans Cybern. 2015;45(3):497–505.
  • Zhang CL, Li JM. Adaptive iterative learning control of non-uniform trajectory tracking for strict feedback nonlinear time-varying systems with unknown control direction. Appl Math Model. 2015;39:2942–2950.
  • Precup RE, Radac MB, Roman RC, et al. Model-free sliding mode control of nonlinear systems: algorithms and experiments. Inf Sci. 2017;381:176–192.
  • Raj R, Mohan BM. Stability analysis of general Takagi–Sugeno fuzzy two-term controllers. Fuzzy Inf Eng. 2018;10(2):196–212.
  • Zhang X, Zhang HG, Sun QY, et al. Adaptive dynamic programming-based optimal control of unknown nonaffine nonlinear discrete-time systems with proof of convergence. Neurocomputing. 2012;35:48–55.
  • Eftekhari M, Zeinalkhani M. Extracting interpretable fuzzy models for nonlinear systems using gradient-based continuous ant colony optimization. Fuzzy Inf Eng. 2013;5(3):255–277.
  • Liu D, Wang D, Yang X. An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs. Inf Sci. 2013;220(20):331–342.
  • Jiang H, Zhang H. Iterative ADP learning algorithms for discrete-time multi-player games. Artif Intell Rev. 2018;50(1):75–91.
  • Liu D, Wang D, Zhao D, et al. Neural-network-based optimal control for a class of unknown discrete-time nonlinear systems using globalized dual heuristic programming. IEEE Trans Autom Sci Eng. 2012;9(3):628–634.
  • Kiumarsi B, Lewis FL, Modares H, et al. Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics. Automatica. 2014;50(4):1167–1175.
  • Yang Q, Jagannathan S. Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators. IEEE Trans Syst Man Cybern B Cybern. 2012;42(2):377–390.
  • Ha M, Wang D, Liu D. Event-triggered constrained control with DHP implementation for nonaffine discrete-time systems. Inf Sci. 2020;519:110–123.
  • Xu B, Yang C, Shi Z. Reinforcement learning output feedback NN control using deterministic learning technique. IEEE Trans Neural Netw Learn Syst. 2014;25(3):635–641.
  • Liu YJ, Li S, Tong S, et al. Adaptive reinforcement learning control based on neural approximation for nonlinear discrete-time systems with unknown nonaffine dead-zone input. IEEE Trans Neural Netw Learn Syst. 2019;30(1):295–305.
  • Allam E, Elbab HF, Hady MA, et al. Vibration control of active vehicle suspension system using fuzzy logic algorithm. Fuzzy Inf Eng. 2010;2(4):361–387.
  • Niftiyev AA, Zeynalov CI, Poormanuchehri M. Fuzzy optimal control problem with non–linear functional. Fuzzy Inf Eng. 2011;3(3):311–320.
  • Fei J, Wang T. Adaptive fuzzy-neural-network based on RBFNN control for active power filter. Int J Mach Learn Cybern. 2019;10:1139–1150.
  • Treesatayapun C, Uatrongjit S. Adaptive controller with fuzzy rules emulated structure and its applications. Eng Appl Artif Intell. 2005;18:603–615.
  • Treesatayapun C. Adaptive control based on IF–THEN rules for grasping force regulation with unknown contact mechanism. Robot Comput Integr Manuf. 2014;30:11–18.
  • Abouheaf M, Gueaieb W. Neurofuzzy reinforcement learning control schemes for optimized dynamical performance. 2019 IEEE International Symposium on Robotic and Sensors Environments (ROSE). Ontario, Canada; 2019 June. p. 17–18.
  • Treesatayapun C. Fuzzy-rule emulated networks based on reinforcement learning for nonlinear discrete-time controllers. ISA Trans. 2008;47:362–373.
  • Wei Q, Lewis FL, Sun Q, et al. Discrete-time deterministic q-learning: a novel convergence analysis. IEEE Trans Cybern. 2017;47(5):1224–1237.