Research on Intelligent Control Strategy of Multi-motor Drive for Unmanned Monorail Crane

To improve the adaptive control performance of monorail cranes under unbalanced load startup, sudden load change, and system speed regulation, a radial basis function neural network (RBF) combined with proportional-integral-derivative (PID) is proposed as a distributed motor drive control strategy for monorail cranes. First, the overall structure of the monorail crane control system is determined according to the distributed motor drive control characteristics of the monorail crane. Secondly, the proposed RBF-PID control method is analyzed, and the control principle is explained. Eventually, the effectiveness of the proposed control strategy is verified by simulation. Through the real-vehicle testing and comparison of different algorithms, it is further verified that the RBF-PID algorithm has excellent control performance, which provides a good guarantee for the safe traveling of unmanned monorail cranes.