Adaptive Anti-Swing and Positioning Control of Input-Delayed Bridge Cranes Using Hybrid PSO-GWO Optimization

As key equipment in the field of engineering construction and logistics transport, bridge cranes play an important role in material handling operations. The load swinging during operations significantly affects work efficiency and may cause serious safety accidents. In addition, the inherent flexibility of the hoist rope introduces time delays in control inputs, further complicating anti-swing control. To reduce the swing during operations, this paper employs the Padé approximation to transform the time-delayed model of the bridge crane system into a delay-free augmented model. An adaptive controller is then designed to simultaneously achieve load anti-swing control and trolley positioning. Moreover, a hybrid particle swarm optimization-grey wolf optimizer (PSO-GWO) algorithm is used to enhance the control performance. Digital simulations under different parameter conditions demonstrate the effectiveness of the proposed control method. The trolley positioning is achieved within 5 seconds and the steady-state positioning error is essentially eliminated. The load swing angle stabilizes within 8 seconds, while the driving force required for the trolley remains moderate. This method effectively enhances the system's accuracy and response speed. Future research will focus on physical experiments and industrial application of the presented control approach and solving the disturbances from sensor faults or other uncertainties to enhance practicality.