Sliding mode based learning control for interconnected systems

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