摘要
A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.
A new intelligent anti-swing control scheme, which combined fuzzy neural network （FNN） and sliding mode control （SMC） with particle swarm optimization （PSO）, was presented for bridge crane. The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem, lifting-rope subsystem and anti-swing subsystem. Then, the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances. During the process of high-speed load hoisting and dropping, this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties, and the maximum swing angle is only ＋0.1 rad, but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system. The simulation results show the correctness and validity of this method.
基金
Project(51075289) supported by the National Natural Science Foundation of China
Project(20122014) supported by the Doctor Foundation of Taiyuan University of Science and Technology,China
