Abstract
In this paper, the problem of designing a new iterative learning control has been investigated for a class of strict-feedback nonlinear systems subject to both structured and unstructured uncertainties and dynamic disturbances. The considered systems are assumed to perform the same operation repeatedly under alignment condition. Simple learning mechanisms are proposed to estimate the unknown state-dependent nonlinear functions satisfying local Lipschitz conditions. By using the concept of command filtered backstepping, the problem of the explosion of complexity existing in conventional backstepping is eliminated and the proposed controller is greatly simplified. Lyapunov-like functional method is used to prove the boundedness of all signals of the resulting closed-loop system and the convergence of the tracking errors to zero over iterations. Simulation results are provided to show the effectiveness of the proposed control scheme.
Similar content being viewed by others
References
Chen Y.Q., Moore K.L., Yu J., Zhang T.: Iterative learning control and repetitive control in hard disk drive industry-a tutorial. Int. J. Adaptive. Control Signal. Proc. 22, 325–343 (2008)
De Roover D., Bosgra O.H.: Synthesis of robust multivariable iterative learning controllers with application to a wafer stage motion system. Int. J. Control 73, 968–979 (2000)
Longman R.W.: Iterative learning control and repetitive control for engineering practice. Int. J. Control 73, 930–954 (2000)
Norrlof M.: An adaptive iterative learning control algorithm with experiments on an industrial robot. IEEE Trans. Robot. Autom. 18, 245–251 (2005)
Ahn H.S., Moore K.L., Chen. C.Y.: Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems. Springer, New York (2007)
Bu X., Hou Z., Yu F., Fu Z.: Iterative learning control for a class of non-linear switched systems. IET Control Theory Appl. 7, 470–481 (2013)
Madady A.: PID type iterative learning control with optimal gains. Int. J. Control Autom. Syst. 6, 194–203 (2008)
Sun M., Wang D.: Iterative learning control with initial rectifying action. Automatica 38, 1177–1182 (2002)
Xu J.X., Tan Y.: Robust optimal design and convergence properties analysis of iterative learning control approaches. Automatica 1, 1867–1880 (2002)
Yang Z., Chan C.W.: Conditional iterative learning control for non-linear systems with non-parametric uncertainties under alignment condition. IET Control Theory Appl. 3, 1521–1527 (2009)
Chen W., Chen Y.-Q., Yeh C.P.: Robust iterative learning control via continuous sliding-mode technique with validation on an srv02 rotary plant. Mechatronics 40, 855–864 (2012)
Chien C.J.: A combined adaptive law for fuzzy iterative learning control of nonlinear systems with varying control tasks. IEEE Trans. Fuzzy Syst. 16, 40–51 (2008)
Chien C.J., Tayebi A.: Further results on adaptive iterative learning control of robot manipulators. Automatica 44, 830–837 (2008)
French M., Rogers E.: Nonlinear iterative learning by an adaptive lyapunov technique. Int. J. Control 73, 840–850 (2000)
Li X.D., Chow T.W.S., Cheng L.L.: Adaptive iterative learning control of nonlinear MIMO systems with iteration-varying initial error and reference trajectory. Int. J. Syst. Sci. 44, 786–794 (2013)
Tayebi A., Chien C.J.: A unified adaptive iterative learning control framework for uncertain nonlinear system. IEEE Trans. Autom. Control 52, 1907–1913 (2007)
Xu J.X., Xu J.: On iterative learning from deferent tracking task in the presence of times-varying uncertainties. IEEE Trans. Syst. Man. Cybern. Part B Cybern. 34, 589–597 (2004)
Li X.D., Chow T.W.S., Ho J.K.L., Zhang J.: Iterative learning control with initial rectifying action for nonlinear continuous systems. IET Control Theory Appl. 3, 49–55 (2009)
Chen W., Zhang L.: Adaptive iterative learning control for nonlinearly parameterized systems with unknown time-varying delays. Int. J. Control Autom. Syst. 8, 177–186 (2010)
Krstic M., Kanellakopoulos I., Kokotovic P.: Nonlinear and Adaptive Control Design. Wiley, New York (1995)
Yip P.P., Hedrick J.K.: Adaptive dynamic surface control: a simplified algorithm for adaptive backstepping control of nonlinear systems. Int. J. Control 71, 959–979 (1998)
Swaroop D., Hedrick J.K., Yip P.P., Gerdes J.C.: Dynamic surface control for a class of nonlinear systems. IEEE Trans. Autom. Control 45, 1893–1899 (2000)
Tong S.C., Li Y.M., Feng G.: Observer-based adaptive fuzzy backstepping dynamic surface control for a class of MIMO nonlinear systems. IEEE Trans. Syst. Man. Cybern. Part B Cybern. 41, 1124–1135 (2011)
Tong, S.C.; Sui, S.; Li, Y.M.: Fuzzy adaptive output feedback control of MIMO nonlinear systems with partial tracking errors constrained. IEEE Trans. Fuzzy Syst. doi:10.1109/TFUZZ.2014.2327987
Li T.S., Wang D., Feng G., Tong S.C.: A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems. IEEE Trans. Syst. Man. Cybern. Part B Cybern. 40, 915–927 (2010)
Ferrel J.A., Polycarpou M., Charma M., Dong W.: Command filtered backstepping. IEEE Trans. Autom. Control 54, 1391–1395 (2009)
Zong Q., Ji Y.H., wang F.: Input-to-state stability-modular command filtered backstepping control of strict feedback systems. IET Control Theory Appl. 6, 1125–1129 (2012)
Xu J.X., Tan Y.: A composite energy function based learning control approach for nonlinear systems with time-varying parametric uncertainties. IEEE Trans. Autom. Control 47, 1940–1945 (2002)
Li, Y.M.; Tong, S.C.; Li, T.: Observer-based adaptive fuzzy tracking control of MIMO stochastic nonlinear systems with unknown control directions and unknown dead-zones. IEEE Trans. Fuzzy Syst. doi:10.1109/TFUZZ.2014.2348017
Polycarpou M.M., Ioannou P.A.: A robust adaptive nonlinear control design. Automatica 32, 423–427 (1996)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Benslimane, H., Boulkroune, A. & Chekireb, H. Iterative Learning Control for Strict-Feedback Nonlinear Systems with Both Structured and Unstructured Uncertainties. Arab J Sci Eng 41, 3683–3694 (2016). https://doi.org/10.1007/s13369-015-1901-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-015-1901-9