Event-based model-free adaptive control for discrete-time non-linear processes

Event-based model-free adaptive control for discrete-time non-linear processes

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In this study, a novel event-based model-free adaptive control (MFAC) algorithm for discrete-time non-linear systems is presented. Different from the traditional MFAC scheme which calculates the control signal at fixed sampling instants, an event-based sampling scheme is given to calculate the new control signal only when the input/output (I/O) data sufficiently changes. The event-triggered MFAC can obviously reduce the computational load and network communication. The closed-loop system is proven to be ultimately bounded by using the Lyapunov technique. Finally, the simulation examples indicate the effectiveness and applicability of the proposed event-trigger model-free adaptive control algorithm.


    1. 1)
      • 1. Chai, T.Y., Hou, Z.S., Lewis, F.L., et al: ‘Guest editorial data-based control modeling, and optimization’, IEEE Trans. Neural Netw., 2011, 22, pp. 21502153.
    2. 2)
      • 2. Hou, Z.S., Wang, Z.: ‘From model-based control to data-driven control: survey, classification and perspective’, Inf. Sci., 2011, 235, pp. 21732188.
    3. 3)
      • 3. Yin, S., Li, X., Gao, H., et al: ‘Data-based techniques focused on modern industry: an overview’, IEEE Trans. Ind. Electron., 2015, 62, pp. 657667.
    4. 4)
      • 4. Hou, Z.S., Jin, S.T.: ‘Data driven model-free adaptive control for a class of MIMO nonlinear discrete time systems’, IEEE Trans. Neural Netw., 2011, 22, pp. 21732188.
    5. 5)
      • 5. Hou, Z.S., Jin, S.T.: ‘A novel data-driven control approach for a class of discrete-time nonlinear systems’, IEEE Trans. Control Syst. Technol., 2011, 19, pp. 15491558.
    6. 6)
      • 6. Zhang, H., Liu, D., Luo, Y., et al: ‘Adaptive dynamic programming for control-algorithms and stability’ (Springer-Verlag, London, UK, 2013).
    7. 7)
      • 7. Lewis, F.L., Vrabie, D., Vamvoudakis, K.G.: ‘Reinforcement learning and feedback control: using natural decision methods to design optimal adaptive controllers’, IEEE Control Syst., 2012, 32, pp. 76105.
    8. 8)
      • 8. Fan, Q.Y., Yang, G.H.: ‘Adaptive actor-critic design-based integral sliding-mode control for partially unknown nonlinear systems with input disturbances’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, pp. 165177.
    9. 9)
      • 9. Astrom, K.J., Hagglund, T., Wallenborg, A.: ‘Automatic Tuning of PID Controllers’ (Instrum. Soc. Amer., Research Triangle Park, NC, USA, 1988).
    10. 10)
      • 10. Xu, J.X.: ‘Linear and nonlinear iterative learning control’ (Springer-Verlag, Berlin, Germany, 2003).
    11. 11)
      • 11. Campi, M.C., Savaresi, S.M.: ‘Direct nonlinear control design: the virtual reference feedback tuning (VRFT) approach’, IEEE Trans. Autom. Control, 2006, 51, pp. 1427.
    12. 12)
      • 12. Sala, A., Esparza, A.: ‘Extensions to virtual reference feedback tuning: a direct method for the design of feedback controllers’, Automatica, 2005, 41, pp. 14731476.
    13. 13)
      • 13. Safonov, M.G., Tsao, T.C.: ‘The unfalsified control concept and learning’, IEEE Trans. Autom. Control, 1997, 42, pp. 843847.
    14. 14)
      • 14. Bontempi, G., Birattari, M.: ‘From linearization to lazy learning: a survey of divide-and-conquer techniques for nonlinear control (Invited Paper)’, Int. J. Comput. Cogn., 2005, 3, pp. 5673.
    15. 15)
      • 15. Hou, Z.S., Jin, S.T.: ‘Model free adaptive control: theory and applications’ (CRC Press, Boca Raton, FL, USA, 2013).
    16. 16)
      • 16. Zhu, Y.M., Hou, Z.S.: ‘Data-driven MFAC for a class of discrete-time nonlinear systems with RBFNN’, IEEE Trans. Neural Netw. Learn. Syst., 2014, 25, pp. 10131020.
    17. 17)
      • 17. Hou, Z.S., Zhu, Y.M.: ‘Controller-dynamic-linearization-based model free adaptive control for discrete-time nonlinear systems’, IEEE Trans. Ind. Inf., 2013, 9, pp. 23012309.
    18. 18)
      • 18. Zhu, Y.M., Hou, Z.S.: ‘Controller dynamic linearization-based model-free adaptive control framework for a class of non-linear systems’, IET Control Theory Appl., 2015, 9, pp. 11621172.
    19. 19)
      • 19. Xu, D.Z., Jiang, B., Shi, P.: ‘A novel model-free adaptive control design for multivariable industrial processes’, IEEE Trans. Ind. Electron., 2014, 61, pp. 63916398.
    20. 20)
      • 20. Xu, D.Z., Jiang, B., Shi, P.: ‘Adaptive observer based data-driven control for nonlinear discrete-time processes’, IEEE Trans. Autom. Sci. Eng., 2014, 11, pp. 10371045.
    21. 21)
      • 21. Heemels, W.P.M.H., Donkers, M.C.F.: ‘Model-based periodic event-triggered control for linear systems’, Automatica, 2013, 49, pp. 698711.
    22. 22)
      • 22. Heemels, W.P.M.H., Sandee, J.H., Van Den Bosch, P.P.J.: ‘Analysis of event-driven controllers for linear systems’, Int. J. Control, 2008, 81, pp. 571590.
    23. 23)
      • 23. Li, Y.X., Yang, G.H.: ‘Model-based adaptive event-triggered control of strict-feedback nonlinear systems’, IEEE Trans. Neural Netw. Learn. Syst., 2017, DOI: 10.1109/TNNLS.2017.2650238.
    24. 24)
      • 24. Sahoo, A., Xu, H., Jagannathan, S.: ‘Near optimal event-triggered control of nonlinear discrete-time systems using neurodynamic programming’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, pp. 18011815.
    25. 25)
      • 25. Sahoo, A., Xu, H., Jagannathan, S.: ‘Adaptive neural network-based event-triggered control of single-input single-output nonlinear discrete time systems’, IEEE Trans. Neural Netw. Learn. Syst., 2016, 27, pp. 151164.
    26. 26)
      • 26. Arzen, K.: ‘A simple event-based PID controller’. Proc. 14th IFAC World Congress, 1999, vol. 18, pp. 423428.
    27. 27)
      • 27. Durand, S., Marchand, N.: ‘Further results on event-based PID controller’. Proc. European Control Conf., 2009, pp. 19791984.
    28. 28)
      • 28. Jagannathan, S.: ‘Neural network control of nonlinear discrete-time systems’ (CRC Press, Boca Raton, FL, USA, 2006).
    29. 29)
      • 29. Chen, C.L.P., Wen, G.X., Liu, Y.J., et al: ‘Adaptive consensus control for a class of nonlinear multiagent time-delay systems using neural networks’, IEEE Trans. Neural Netw. Learn. Syst., 2016.
    30. 30)
      • 30. Zhang, X., Wu, L.G., Han, Y.Y., et al: ‘State estimation for delayed genetic regulatory networks with reaction-diffusion terms’, IEEE Trans. Neural Netw. Learn. Syst., 2016, DOI: 10.1109/TNNLS.2016.2618899, in press.
    31. 31)
      • 31. Liu, Y.J., Tong, S.C., Chen, C.L.P., et al: ‘Neural controller design-based adaptive control for nonlinear MIMO systems with unknown hysteresis inputs’, IEEE Trans. Cyber., 2016, 46, pp. 919.
    32. 32)
      • 32. Liu, Y.J., Tong, S.C.: ‘Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems’, Automatica, 2017, 76, pp. 143152.
    33. 33)
      • 33. Zhu, J.W., Yang, G.H., Wang, H., et al: ‘Fault estimation for a class of nonlinear systems based on intermediate estimator’, IEEE Trans. Autom. Control, 2016, 27, pp. 25182524.
    34. 34)
      • 34. Eskinat, E., Johnson, S.: ‘Use of Hammerstein models in identification of nonlinear systems’, AIChE J., 1991, 37, pp. 255268.

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