Use of multiobjective genetic algorithms to optimize inter-vehicle active suspensions

This paper studies inter-vehicle active suspensions for railway vehicles and presents an optimization process for the design of vertical active suspension controllers using multiobjective genetic algorithms. A three-vehicle train set is used in the study and two active control schemes are considered primarily to provide the best improvement in the passenger ride quality. The first scheme uses only actuators placed between adjacent vehicles while the second adds two actuators between bogie and vehicle body at either end of the train set in addition to the inter-vehicle actuators. The development of the control laws is assisted by the use of genetic algorithms to achieve the 'best' compromise of different design criteria, especially that between the ride quality and the suspension deflections. The study shows that, when the control laws for the proposed active schemes are optimized, a significant improvement in the vertical ride quality on random tracks is obtained and in the mean time the suspension deflections can be kept within their allowed clearance when the vehicles run on to a gradient.