Artificial-neural-network-based optimal Smoother design for oscillation suppression control of underactuated overhead cranes with distributed mass beams

As a kind of convenient and practical oscillation suppression method, the open-loop controllers are widely utilized in the control of actual cranes, yet most of them need to be designed for each mode frequency when targeting multimodal systems, which will degrade the robustness of the controller. Moreover, most open-loop controllers require linearization of the model in the design process, which will inevitably reduce the performance of the controller in terms of oscillation suppression. To solve the above prevalent problems, an optimal command-smoothing method is proposed in this paper. Specifically, the dynamic analysis of a 2-D overhead crane with distributed-mass beams (DMB) is first performed. Then, the particle swarm optimization algorithm (PSO) is used to solve the optimal values of the parameters of the Smoother under different system parameters, and a data set for ANN training is generated, together with a series of indices to verify the effectiveness of the designed ANN. Finally, in combination with the PD controller, the proposed controller is experimentally proven to accomplish the positioning requirements while improving the oscillation suppression efficiency by an average of 29% compared to the basic Smoother.