Application of adaptive neural fuzzy reasoning system in crane hook control

adaptive neural fuzzy reasoning system;  anti-sway control;  Blended learning algorithms

The crane trolley-lifting system has the characteristics of multivariation, nonlinearity and uncertainty of wire rope length, which makes it difficult to control the rocking during the movement of the lifting object. A control method based on adaptive neural fuzzy reasoning system (ANFIS) is proposed to be applied to the stable hook control of cranes, which uses a hybrid algorithm of backpropagation algorithm (BP) and least squares algorithm (LS) to learn the sample data of the trolley-lifting system, adjust the membership function of each variable, and automatically generate fuzzy rules. The simulation results show that this control method has good dynamic performance and strong robustness for the control process of the rocking angle and trolley position of the trolley-lifting system, which shows the effectiveness of the adaptive neural fuzzy reasoning system in the crane steady control.