Crane Adaptive Intelligent Anti-sway Control Method And Its Simulation Study

Eliminating and controlling the swing of loads is very important for increasing the work efficiency of crane as well as decreasing safety hazard during loading & unloading operation. It's a main method to lighten the work strength and to improve the bad work condition for the operator by adopting electronic anti-swing device, which also the trend of actualizing the loading & unloading machinery automation.The state feedback, PID control, LQR optimal control methods were used in this study for the simulation of the crane anti-swing issues, the "speed follow-up" and the "speed & displacement double follow-up" fuzzy control methods were developed based on learning and studying fuzzy control and fuzzy neural network theory as well as considering the project practice and convenient for driver operation, the simulation result showed that: fuzzy control does either realize the accurate position of the trolley, or control the loads swing. However, there is a significant steady-state error for trolley position when initial disturbance exists. Based on fuzzy anti-swing controller, further studying fuzzy neural network control methods which have both advantage of the fuzzy control and neural network, then the T-S self-adaptive fuzzy neural network controller was designed, which succeed virtue of fuzzy controller, also mostly eliminate the steady-state error during initial disturbance. The study showed that the T-S self-adaptive fuzzy neural network controller is an effective anti-swing method.The Innovation results for this study were shown as follows:(1) Establishing the absolute and non-linear dynamics equation for crane double-way anti-swing by using Lagrange method could lay the theory foundation in studying crane double-way anti-swing problem.(2)The simulation for crane anti-swing issues was carried out by separately adopting the state feedback, PID control and LQR Optimal Control methods.(3) The "speed & displacement double follow-up" fuzzy controllers was firstly created with the ideal anti-swing efficiency without initial disturbance.(4) The fuzzy technology, neural network technology and LQR Optimal Control method were integrated to design T-S type self-adaptive fuzzy neural network controller, which can resolve the significant steady-state error for trolley position when existing initial disturbance.