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| dc.contributor.author | Esaias, Abera | |
| dc.date.accessioned | 2022-12-31T07:15:37Z | |
| dc.date.available | 2022-12-31T07:15:37Z | |
| dc.date.issued | 2022-10 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/14797 | |
| dc.description.abstract | Magnetic levitation system is a method of suspending an object in the air using the principle of magnetic attraction or repulsion force. It has been applied in various applications where contact free operation is a primary design consideration. A Maglev train is an advanced train that uses a magnetic levitation system to float smoothly from starting point to destination in the air without ever touching the guideway track. The main objective of this thesis is to design genetic algorithm tuned sliding mode controller for suspension of Maglev train with flexible track. The suspension stability can be easily impacted with minor deformation, especially when the track stiffness is low, the Maglev train will result in smashing with guidance track. The dynamic model was developed by studying single point electromagnetic suspension and pier supported beam with a span to analyze the influence of flexible track on the suspension system. For controlling the Maglev train suspension air-gap displacement, the sliding mode control is designed to ensure a safe ride by eliminating rail smashing caused by flexible track. Furthermore, to overcome the chattering problem due to conventional sliding mode control, the super twisting sliding mode controller is designed. Genetic algorithm is applied for tuning the sliding surface coefficients and controller gain parameters to obtain the optimum value. The proposed controllers are tested under different circumstances, i.e., Maglev train with rigid track, Maglev train with flexible track under different load sizes, under external random disturbance and under variable suspension air-gap displacement. Based on performance characteristics for a Full-load Maglev train with flexible track genetic algorithm tuned sliding mode control has 0.0565𝑡𝑓𝑑 settling time, 2.9956% overshoot and 0.0254𝑡𝑓𝑑 rise time at 1.5798 ร 10 โ5 ITAE value and genetic algorithm tuned super twisting sliding mode control has 0.0529𝑡𝑓𝑑 settling time, 2.4125% overshoot and 0.031𝑡𝑓𝑑 rise time at 3.1343 ร 10 โ6 ITAE value for tracking 9𝑛𝑛 suspension air-gap displacement. Keywords: Flexible track, Genetic algorithm, Maglev train, Sliding mode control, Super twisting sliding mode control and Suspension air-gap. | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | ELECTRICAL AND COMPUTER ENGINEERING | en_US |
| dc.title | Genetic Algorithm Tuned Sliding Mode Controller For Suspension of Maglev Train With Flexible Track | en_US |
| dc.type | Thesis | en_US |
