Neural sliding mode control on suspension gap for single electromagnetic guiding actuator of linear elevator
Control and Decision Conference（2012）
摘要
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.
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关键词
Lyapunov methods,adaptive control,electromagnetic actuators,learning (artificial intelligence),lifts,neurocontrollers,radial basis function networks,stability,suspensions (mechanical components),variable structure systems,Matlab simulation,RBF neural network,chattering phenomenon,high-frequency differential controller,learning function,linear elevator system,linear lift,maglev guiding actuator system,neural network convergence,neural network-based adaptive sliding mode control method,parallel control part,single electromagnetic guiding actuator,single electromagnetic suspension device,suspension altitude control,suspension gap,switching part,system parameter variation,tracking performance,uncertain parameters compensation,Adaptive,Linear Elevator,Maglev Guiding Actuator,RBF Neural Network,Sliding Mode Control,
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