Genetic Algorithm Tuned Super Twisting Sliding Mode Controller for Suspension of Maglev Train With Flexible Track
Esaias Abera Teklu, Chala Merga Abdissa
Abstract
The suspension air-gap of Maglev train needs controller for it is inherently unstable and highly nonlinear. To maintain a high quality of ride, comfort and safety for the passengers the train suspension controller must maintain the suspension air-gap. During operation the suspension air-gap of a Maglev train is only in millimeters. This easily causes instability under excitation of nonlinear load and track tiny deformation. Additionally, a track with a low stiffness will experience larger bending displacement and result in high vibrations. A high stiffness track experience minor bending displacement, which will diminish the vibration and improve riding quality. Unfortunately, using high stiffness guideway track is expensive. Instead, by designing a good controller the low stiffness material can be used. In this paper genetic algorithm tuned super twisting sliding mode control is proposed for it offers a good controlling ability since it is insensitive to external disturbance, parameter variation and has a fast dynamic response. The proposed controllers are tested under different circumstances, i.e., Maglev train with rigid track and with flexible track under variable load and external random disturbance.