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...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.展开更多
Maximum regulated takeoff weights and hence payloads of large commercial jets are limited by government regulations which take into account local airport conditions as well as a variety of safety factors. One of the c...Maximum regulated takeoff weights and hence payloads of large commercial jets are limited by government regulations which take into account local airport conditions as well as a variety of safety factors. One of the challenging conditions that must be met is linked to a minimum obstacle clearance in the unlikely event of an engine failure on the runway at the worst possible time. This requirement becomes an overriding factor for airports surrounded by challenging terrain, and therefore a well defined takeoff path out of these airports has the potential to transform a financially unsustainable operation into a commercially viable one. The research described in this paper represents an ongoing attempt to resolve this important problem and makes use of recent advances in robot path planning techniques.展开更多
基金Project(51075289) supported by the National Natural Science Foundation of ChinaProject(20122014) supported by the Doctor Foundation of Taiyuan University of Science and Technology,China
文摘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.
文摘Maximum regulated takeoff weights and hence payloads of large commercial jets are limited by government regulations which take into account local airport conditions as well as a variety of safety factors. One of the challenging conditions that must be met is linked to a minimum obstacle clearance in the unlikely event of an engine failure on the runway at the worst possible time. This requirement becomes an overriding factor for airports surrounded by challenging terrain, and therefore a well defined takeoff path out of these airports has the potential to transform a financially unsustainable operation into a commercially viable one. The research described in this paper represents an ongoing attempt to resolve this important problem and makes use of recent advances in robot path planning techniques.
