Nikiforov
Vladimir O.
D.Sc., Prof.
doi: 10.17586/2226-1494-2016-16-1-61-67
BACKSTEPPING ALGORITHM FOR LINEAR SISO PLANTS UNDER STRUCTURAL UNCERTAINTIES
Read the full article ';
For citation: Furtat I.B., Nekhoroshikh A.N. Backstepping algorithm for linear SISO plants under structural uncertainties. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 1, pp. 61–67.
Abstract
The robust algorithm is proposed for parametric and structurally uncertain linear plants under external bounded disturbances. The structural uncertainty is an unknown dynamic order of the model of plants. The developed algorithm provides plant output tracking for a smooth bounded reference signal with a required accuracy at a finite time. It is assumed that only scalar input and output of the plants are available for measurement, but not their derivatives. For the synthesis of the control algorithm we use a modified backstepping algorithm. The synthesis of control algorithm is separated into rsteps, where ris an upper bound of the relative degree of control plant model. At each step we synthesize auxiliary controls that stabilize each subsystem about a zero. At the last step we synthesize a basic control law, which provides output tracking for smooth reference signal. It is shown that for the implementation of the algorithm we need to use only one filter of the control signal and the simplified control laws obtained by application of the real derivative elements. It allows simplifying significantly the calculation and implementation of the control system. Numerical examples and results of computer simulation are given, illustrating the operation of the proposed scheme.
Acknowledgements. The control algorithm proposed in Section “Method of solution” was developed at the Institute of Problems of Mechanical Engineering, Russian Academy of Sciences and supported by a grant from the Russian Science Foundation (project No. 14-29-00142). The other researches were partially financially supported by a grant of the Ministry of Education and Science of the Russian Federation (project No. 14.Z50.31.0031) and the Government of the Russian Federation (074-U01).
References
1. Miroshnik I.V., Nikiforov V.O., Fradkov A.L. Nelineinoe i Adaptivnoe Upravlenie Slozhnymi Dinamicheskimi Sistemami [Nonlinear and Adaptive Control of Complex Dynamic Systems]. St. Petersburg, Nauka Publ., 2000, 549 p.
2. Kokotovic P.V. The joy of feedback: nonlinear and adaptive. IEEE Control Systems Magazine, 1992, vol. 12, no. 3, pp. 7–17. doi: 10.1109/37.165507
3. Khalil H. Nonlinear Systems. 3rd ed. Upper Saddle River, Prentice Hall, 2002, 750 p.
4. Nikiforov V.O. Modified adaptive controllers for linear plants with improved parametric robustness and transient performance. Proc. 4th European Control Conference. Brussels, Belgium, 1997.
5. Kanellakopoulos I., Kokotovic P.V., Morse A.S. Systematic design of adaptive controllers for feedback linearizable systems. IEEE Transactions on Automatic Control, 1991, vol. 36, no. 11, pp. 1241–1253. doi: 10.1109/9.100933
6. Krstic M., Kanellakopoulos I., Kokotovic P.V. Passivity and parametric robustness of new class of adaptive systems. Automatica, 1994, vol. 30, no. 11, pp. 1703–1716. doi: 10.1016/0005-1098(94)90073-6
7. Krstic M., Kanellakopoulos I., Kokotovic P.V. Nonlinear design of adaptive controllers for linear systems. IEEE Transactions on Automatic Control, 1994, vol. 39, no. 4, pp. 738–752. doi: 10.1109/9.286250
8. Zheng Y.F., Yang Y.S. Adaptive output feedback control for class of nonlinear systems with unknown virtual control coefficients signs. International Journal of Adaptive Control and Signal Processing, 2007, vol. 21, no. 1, pp. 77–89. doi: 10.1002/acs.925
9. Freeman R.A., Kokotovic P.V. Design of softer robust nonlinear control laws. Automatica, 1993, vol. 29, no. 6, pp. 1435–1437. doi: 10.1016/0005-1098(93)90007-G
10. Chen W.S., Jiao L.C., Wu J.S. Decentralized backstepping output-feedback control for stochastic interconnected systems with time-varying delays using neural networks. Neural Computing & Applications, 2012, vol. 21, no. 6, pp. 1375–1390. doi: 10.1007/s00521-011-0590-x
11. Zhou J. Decentralized adaptive backstepping stabilization of interconnected systems with input time delays in dynamic interactions. International Journal of Adaptive Control and Signal Processing, 2012, vol. 26, no. 4, pp. 285–301. doi: 10.1002/acs.1281
12. Tong S.C., Li Y.M., Zhang H.G. Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays. IEEE Transactions on Neural Networks, 2011, vol. 22, no. 7, pp. 1073–1086. doi: 10.1109/TNN.2011.2146274
13. Bresch-Pietri D., Chauvin J., Petit N. Adaptive backstepping for uncertain systems with time-delay on-line update laws. Proc. 2011 American Control Conference. San Fransisco, USA, 2011, pp. 4890–4897.
14. Furtat I.B. Modified algorithm of robust integrator backsteping. Mekhatronika, Avtomatizatsiya, Upravlenie, 2009, no. 10, pp. 2–7. (In Russian)
15. Furtat I.B., Tupichin E.A. Control of nonlinear plant based on modified robust backstepping algorithm. Proc. 2014 IEEE Int. Conf. on Control Applications, CCA 2014. Antibes, France, 2014, pp. 941–946.
16. Tao G., Ioannou P.A. Model reference adaptive control for plants with unknown relative degree. IEEE Transactions on Automatic Control, 1993, vol. 38, no. 6, pp. 976–982. doi: 10.1109/9.222314
17. Hoagg J.B., Bernstein D.S. Direct adaptive command following and disturbance rejection for minimum phase systems with unknown relative degree. International Journal of Adaptive Control and Signal Processing, 2007, vol. 21, no. 1, pp. 49–75. doi: 10.1002/acs.945
18. Fradkov A.L. Upravlenie v Slozhnykh Sistemakh [Control of Complex Systems]. Moscow, Nauka Publ., 1990, 296 p.
19. Tsykunov A.M. Robust control algorithms with compensation of bounded perturbations. Automation and Remote Control, 2007, vol. 68, no. 7, pp. 1213–1224. doi: 10.1134/S0005117907070090