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
In this paper, we study the problems related to parameter estimation of a single-input and single-output networked control system, which contains possible network-induced delays and packet dropout in both of sensor-to...In this paper, we study the problems related to parameter estimation of a single-input and single-output networked control system, which contains possible network-induced delays and packet dropout in both of sensor-to-controller path and controller-to-actuator path. A weighted least squares(WLS) method is designed to estimate the parameters of plant, which could overcome the data uncertainty problem caused by delays and dropout. This WLS method is proved to be consistent and has a good asymptotic property. Simulation examples are given to validate the results.展开更多
A self-adaptive control method is proposed based on an artificial neural network(ANN)with accelerated evolutionary programming(AEP)algorithm.The neural network is used to model the uncertainty process,from which the t...A self-adaptive control method is proposed based on an artificial neural network(ANN)with accelerated evolutionary programming(AEP)algorithm.The neural network is used to model the uncertainty process,from which the teacher signals are produced online to regulate the parameters of the controller.The accelerated evolutionary programming is used to train the neural network.The experiment results show that the method can obviously improve the dynamic performance of uncertainty systems.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.
基金Supported by the National Natural Science Foundation of China(61290324)
文摘In this paper, we study the problems related to parameter estimation of a single-input and single-output networked control system, which contains possible network-induced delays and packet dropout in both of sensor-to-controller path and controller-to-actuator path. A weighted least squares(WLS) method is designed to estimate the parameters of plant, which could overcome the data uncertainty problem caused by delays and dropout. This WLS method is proved to be consistent and has a good asymptotic property. Simulation examples are given to validate the results.
基金Key Equipment Project of China Petroleum & Chemical Corporation(SINOPEC)(No.J W05008)
文摘A self-adaptive control method is proposed based on an artificial neural network(ANN)with accelerated evolutionary programming(AEP)algorithm.The neural network is used to model the uncertainty process,from which the teacher signals are produced online to regulate the parameters of the controller.The accelerated evolutionary programming is used to train the neural network.The experiment results show that the method can obviously improve the dynamic performance of uncertainty systems.
