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This paper presents a data-driven control design framework to achieve robust tracking without exploiting mathematical model of nonlinear underactuated mechanical systems (UMS). The method leverages differential flatness property of linearised systems, online estimation and compensation of disturbances by active disturbance rejection control. The differentially flat output is derived directly from measured data with unknown dynamics and parameters by the flat output identification algorithm. A reduced nominal model of UMS is proposed to simplify the process of finding flat output and trajectory planning. Technique of sparse regression is applied to identify relationships between flat output and system states, which reduce the order of the well-known extended state observer (ESO) and thereby make the ESO more effective for trajectory planning and tracking in terms of the flat output. The proposed control is validated by experimental studies of a rotary crane system in which a rest-to-rest tracking control is implemented.

Data-driven robust tracking control of underactuated mechanical systems using identified flat output and active disturbance rejection control