Neural Sliding Mode Control on Suspension Gap for Single Electromagnetic Guiding Actuator of Linear Elevator
The maglev guiding technology is applied to linear elevator system. Consider the system parameter variation and extra disturbance for maglev guiding actuator system influence. A neural network based adaptive sliding mode control method is proposed to control suspension altitude for maglev guiding system of linear elevator. Adopting RBF neural network and utilizing its learning function to compensate uncertain parameters of the single electromagnetic suspension device of linear lift adaptively could replace the switching part of conventional sliding mode control and eliminate the chattering phenomenon with high-frequency. The proportional and differential controller is designed as one parallel control part, which improves the convergence of neural network, and enhances system stability. The stability of the system was proved by lyapunov theory. Matlab Simulation results show that the proposed control scheme shows good tracking performance and strong robustness.
Linear Elevator Sliding Mode Control RBF Neural Network Adaptive Maglev Guiding Actuator
Qing Hu Mingliang Hao Dongmei Yu
School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
2740-2744
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）