Random Sequence Model for Nonlinear Systems with Unknown Control Direction
Chapter
First Online:
Abstract
The iterative learning control is constructed for the discrete-time networked nonlinear systems with random data dropout at the measurement side and unknown control direction, which have not been studied simultaneously in literature. A novel regulating approach based on truncations is introduced to make the proposed algorithm find the correct control direction adaptively, and then guarantee the almost sure convergence property.
References
- 1.Jiang, P., Chen, H., Bamforth, C.A.: A universal iterative learning stabilizer for a class of MIMO systems. Automatica 42(6), 973–981 (2006)MathSciNetCrossRefGoogle Scholar
- 2.Ahn, H.-S., Chen, Y.Q., Moore, K.L.: Iterative learning control: Survey and categorization from 1998 to 2004. IEEE Trans. Syst. Man Cybern. Part C 37(6), 1099–1121 (2007)CrossRefGoogle Scholar
- 3.Xu, J.-X., Yan, R.: On initial conditions in iterative learning control. IEEE Trans. Autom. Control 50(9), 1349–1354 (2005)MathSciNetCrossRefGoogle Scholar
- 4.Shen, D.: Iterative Learning Control for Nonlinear Stochastic Systems. Ph.D. thesis, Chinese Academy of Sciences (2010)Google Scholar
- 5.Shen, D., Wang, Y.: ILC for networked nonlinear systems with unknown control direction through random lossy channel. Syst. Control Lett. 77, 30–39 (2015)MathSciNetCrossRefGoogle Scholar
Copyright information
© Springer Nature Singapore Pte Ltd. 2018