Iterative Super-Twisting Sliding Mode Control for Tray Indexing System With Unknown Dynamics

Tray indexing driven by a timing-belt actuator is commonly used in the manufacturing industry, in situations where precise and fast reference tracking is required to ensure product quality and manufacturing efficiency. However, as a commonly encountered challenge, several factors make accurate system identification here to be rather difficult, such as the existence of flexible modes and other unmodeled dynamics in the system model, including the deadzone and backlash caused by the friction. Due to these unknown dynamics, the system performance (when system identification is not suitably accurate) could be diminished when traditional model-based controllers are used. To overcome the above-mentioned problems arising from these uncertainties/perturbations and further improve the tracking performance, this work investigates the design of an intelligent model-free controller for such a tray indexing system. Specifically, a 2-degree-of-freedom control structure is developed, which consists of a generalized super-twisting sliding mode control and a unique iterative learning law, and appropriately combined. The proposed method solely makes use of the input–output data obtained during iterative experiments such that the system is driven toward proper satisfactory performance without any prior knowledge of the system parameters. Also, in our work here, rigorous proof is furnished on the convergence properties of the method, and real-time experiments are conducted on the timing-belt stage to validate the effectiveness and the practical appeal of the proposed approach. It is pertinent to note also that applications of the proposed methodology are certainly not constrained to such an industrial timing-belt application only, and its potential can be further deployed advantageously to other appropriate mechatronic systems with similar difficulties involving such existence of uncertainties/perturbations.