A data-driven approach for integral sliding mode control design

In this paper, in light of the recent advances in direct data-driven control, a novel approach for the design of integral sliding mode control (ISMC) in the case of unknown model of the plant is devised. The proposed data-driven integral sliding mode control (DD-ISMC) relies on the so-called virtual reference feedback tuning (VRFT) approach to select the ideal control component of the classical ISMC law. The VRFT enables the selection of the ideal controller by exclusively exploiting data from experiments, through the solution of a global model-reference optimization problem. Then, the reference model exploited by the VRFT approach is employed in the design of the integral sliding variable, in place of the unknown nominal model of the plant. In the paper, the proposal is theoretically analyzed, and its effectiveness is illustrated in simulation.