Abstract
This chapter deals with tracking problem of single-input single-output linear time-invariant (SISO LTI) system with parametric uncertainties by using a model reference adaptive iterative learning control (MRAILC) scheme. The tracking error converges to zero pointwisely after infinite iterations. A major feature of this proposed method is that system output could track the desired trajectory whether the plant system is minimum-phrase or not.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xiong ZH, Zhang J, Wang X, Xu YM (2007) Integrated tracking control strategy for batch processes using a batch-wise linear time-varying perturbation model. IET Control Theory Appl 1:179–188
Hou ZS, Xu JX, Zhong HW (2007) Freeway traffic control using iterative learning control based ramp metering and speed signaling. IEEE Trans Veh Technol 56:466–477
Wang YQ, Zhou DH, Gao FR (2008) Iterative learning reliable control of batch processes with sensor faults. Chem Eng Sci 63:1039–1051
Hahn B, Oldham K (2012) On-off iterative adaptive controller for low-power micro-robotic step regulation. Asian J Control 14:624–640
Chi R, Wang D, Hou Z, Jin S, Zhang D (2012) A discrete-time adaptive iterative learning from different reference trajectory for linear time-varying systems. In: Proceedings of the 31st Chinese control conference
Xu JX, Xu J (2004) On iterative learning from different tracking tasks in the presence of time-varying uncertainties. IEEE Trans Syst Man Cybern 34:589–597
Chi R, Hou Z, Wang D (2013) Discrete-time adaptive ILC for non-parametric uncertain nonlinear systems with iteration-varying trajectory and random initial condition. Asian J Control 15:562–570
Chi R, Sui S, Hou Z (2008) A new discrete-time adaptive ILC for nonlinear systems with time-varying parametric uncertainties. Acta Automatic Sinica 34:805–808
Xu J, Tan Y (2002) A composite energy function-based learning control approach for nonlinear systems with time-varying parametric uncertainties. IEEE Tran Autom Control 47:1940–1945
Tayebi A (2006) Model reference adaptive iterative learning control for linear systems. Int J Adapt Control Signal Process 20:475–489
Acknowledgement
This paper was supported by the National Natural Science Foundation of China (NSFC: 61473010).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Du, W. (2016). On Indirect Model Reference Adaptive Learning Control. In: Jia, Y., Du, J., Li, H., Zhang, W. (eds) Proceedings of the 2015 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48386-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-662-48386-2_27
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-48384-8
Online ISBN: 978-3-662-48386-2
eBook Packages: EngineeringEngineering (R0)