Adaptive Sliding Mode Control for A Tower Crane Considering Model Uncertainty

Cranes are widely used transportation machines in various fields, such as construction sites and factories. Many control methods including those for crane systems rely on mathematical models of the system. However, uncertainty in the model makes the controller design process much more difficult. This paper presents a control method with reduced model dependence by modeling part of the system as an uncertainty. This paper also demonstrates that adding an integral term to the sliding variable can mitigate the effects of calibration error in crane. Lyapunov-based analysis demonstrates that the sliding variable converges to an arbitrary bounds, and the system stability within the bounds demonstrates using the final value theorem and conventional mathematical analysis. Finally, real experimental results validate the efficiency of the proposed controller.