To achieve stabilisation control of an underactuated bridge crane system, a new robust control strategy for the sliding mode is proposed in this paper. It can realise finite-time-convergent stabilisation control under the conditions of model uncertainty, parameter perturbation and external interference. In contrast to the existing methods, our method does not need prior information of the dynamic characteristics of the bridge crane system, and can make the system converge to the equilibrium state at the preset time. Specifically, the nonlinear model of the bridge crane system is linearised with partial feedback, and adaptive signals are introduced. Then, according to the form of the transformed system, a fast terminal sliding mode surface is constructed, and an adaptive terminal sliding mode controller is designed. According to strict analysis, the proposed control law ensures that the system converges to the equilibrium point in finite time and provides the convergence time. Finally, the effectiveness and robustness of the proposed control method are verified by comparing the simulation and experimental results with existing methods.

Anti-swing control of bridge cranes has been widely studied to improve the efficiency of industrial transportation. However, in practice, the performance of bridge crane control methods is reduced by external disturbances and internal uncertainties. Therefore, an enhanced coupled nonlinear control strategy based on feedforward compensation has been proposed in this study. Appropriate composite signals were introduced to enhance the coupling between the system states and improve the transient performance of the controller. The design of the composite signal takes into account the characteristics of crane model. The unknown disturbance was then treated as a state variable, and the crane dynamic model was transformed accordingly. Finally, the state of the model was estimated by the extended state observer, and the disturbance estimate was compensated to improve the robustness of the control system. The stability of the controller was verified through a rigorous mathematical analysis. The simulation and experimental results verified the effectiveness of the proposed method.

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