© The Institution of Engineering and Technology
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.
References
-
-
1)
-
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. 1013–1020.
-
2)
-
27. Durand, S., Marchand, N.: ‘Further results on event-based PID controller’. Proc. European Control Conf., 2009, pp. 1979–1984.
-
3)
-
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. 6391–6398.
-
4)
-
9. Astrom, K.J., Hagglund, T., Wallenborg, A.: ‘Automatic Tuning of PID Controllers’ (Instrum. Soc. Amer., Research Triangle Park, NC, USA, 1988).
-
5)
-
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. 1162–1172.
-
6)
-
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. 657–667.
-
7)
-
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.
-
8)
-
10. Xu, J.X.: ‘Linear and nonlinear iterative learning control’ (Springer-Verlag, Berlin, Germany, 2003).
-
9)
-
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. 56–73.
-
10)
-
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. 2301–2309.
-
11)
-
12. Sala, A., Esparza, A.: ‘Extensions to virtual reference feedback tuning: a direct method for the design of feedback controllers’, Automatica, 2005, 41, pp. 1473–1476.
-
12)
-
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. 143–152.
-
13)
-
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. 14–27.
-
14)
-
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. 9–19.
-
15)
-
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. 2518–2524.
-
16)
-
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. 2150–2153.
-
17)
-
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. 151–164.
-
18)
-
21. Heemels, W.P.M.H., Donkers, M.C.F.: ‘Model-based periodic event-triggered control for linear systems’, Automatica, 2013, 49, pp. 698–711.
-
19)
-
34. Eskinat, E., Johnson, S.: ‘Use of Hammerstein models in identification of nonlinear systems’, AIChE J., 1991, 37, pp. 255–268.
-
20)
-
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. 1801–1815.
-
21)
-
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. 1037–1045.
-
22)
-
15. Hou, Z.S., Jin, S.T.: ‘Model free adaptive control: theory and applications’ (CRC Press, Boca Raton, FL, USA, 2013).
-
23)
-
28. Jagannathan, S.: ‘Neural network control of nonlinear discrete-time systems’ (CRC Press, Boca Raton, FL, USA, 2006).
-
24)
-
26. Arzen, K.: ‘A simple event-based PID controller’. Proc. 14th IFAC World Congress, 1999, vol. 18, pp. 423–428.
-
25)
-
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, .
-
26)
-
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. 1549–1558.
-
27)
-
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, .
-
28)
-
6. Zhang, H., Liu, D., Luo, Y., et al: ‘Adaptive dynamic programming for control-algorithms and stability’ (Springer-Verlag, London, UK, 2013).
-
29)
-
2. Hou, Z.S., Wang, Z.: ‘From model-based control to data-driven control: survey, classification and perspective’, Inf. Sci., 2011, 235, pp. 2173–2188.
-
30)
-
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. 165–177.
-
31)
-
13. Safonov, M.G., Tsao, T.C.: ‘The unfalsified control concept and learning’, IEEE Trans. Autom. Control, 1997, 42, pp. 843–847.
-
32)
-
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. 2173–2188.
-
33)
-
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. 571–590.
-
34)
-
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. 76–105.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2016.1672
Related content
content/journals/10.1049/iet-cta.2016.1672
pub_keyword,iet_inspecKeyword,pub_concept
6
6