Research On GA-Fuzzy Control Of Lateral Semi-active Suspension For High-speed Train

With the rapid development of national economy, there has been a great success in the high-speedrailway industry. At present, china has already had the world’s largest and fastest high-speed railnetworks. In the meantime it caters for the need of the passengers’ convenience, and with the continuousrising in the train speed, the smooth performance of the train is degraded, which makes the higherdemands on the running confort. As passengers are more sensitive to lateral vibration of the train, it isnecessary to apply active or semi-active suspensions to the train in order to improve the smoothperformance of the high-speed train. Compared to the active suspension, semi-active suspension hasadvantages of simple structure, low cost and failure guiding security, moreover, it’s control performanceis close to that of the active suspension. and the corresponding control architecture is more applicable tothe current situation of china’s railway industry. Hence, the use of semi-active suspension system is thebest way to improve the smooth performance of the high-speed train.This thesis focuses on the study of the lateral semi-active suspension for the high-speed train.Firstly, the rigid vehicle dynamic model and the coupled rigid/flexible vehicle dynamic model whichconsiders the lateral elastic vibration were established respectively based on the ADAMS/Rail software.Secondly, on the basis of the dynamic simulation of the passive suspensions associated with two modelsand comparing with each other in their performance, the lateral semi-active suspension system wasproposed and exerted on the two vehicle dynamic models respectively to improve the smoothperformance. By comparing various semi-active control strategies, it is decided to choose fuzzy controlmethod as the control strategy of the lateral semi-active suspension system. Subsequently, a geneticalgorithm is introduced to design the fuzzy controller. The mixed encoding method of float number anddecimal, along with a method of adaptive genetic crossover and mutation probability is used, and themembership functions, the fuzzy rules, the quantification factors and the scaling factors are optimizedsimultaneously to make an improvement of the traditional genetic algorithm. Finally, by selecting theRMS of the lateral vibration at the front and rear of the main body as the performance index, the controlbehavior of the fuzzy controller is evaluated, and the lateral dynamic performance of the semi-activesuspension is achieved satisfactorily.