Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Sliding mode based learning control for interconnected systems

Tay, F.S., Man, Z., Jin, J., Khoo, S.Y., Zheng, J. and Wang, H.ORCID: 0000-0003-2789-9530 (2013) Sliding mode based learning control for interconnected systems. In: 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), 19 - 21 June 2013, Melbourne, VIC, Australia pp. 816-821.

Link to Published Version:
*Subscription may be required


This paper proposes a novel sliding mode based learning control for a class of interconnected systems. The Takagi-Sugeno (T-S) fuzzy modelling technique has been employed to model the interconnected subsystems. Then, based on the fuzzy models, sliding mode learning controllers have been developed to ensure that the closed-loop system is globally asymptotically stable with good tracking performance. Each sliding mode learning controller consists of a most recent control signal and a correction term. The correction term is designed to improve the most recent control signal by incorporating sliding variable information into the control of the subsequent iteration. In addition, a Lipschitz-like condition has been proposed to replace the requirement of information on the upper and the lower bounds of system parameters from the conventional sliding mode control. It differs significantly from the conventional parallel distributed compensation (PDC) in that it does not involve the problem of solving the linear matrix inequality (LMI) for the design of the learning controllers. A simulation example is presented to demonstrate the effectiveness of our proposed control scheme.

Item Type: Conference Paper
Publisher: IEEE
Copyright: © 2013 IEEE
Item Control Page Item Control Page