Intelligent road adaptive suspension system design using an experts' based hybrid genetic algorithm

There is an increasing demand for vehicles suitable for both on and off road driving characterized by superior comfort and handling performance. This is problematic for most suspension systems because there is a trade off balance between vibration reduction, suspension travel, actuator effort, road holding capability, as well as noise and fatigue requirements. Only in the UK every 11 min a car is getting damaged because of potholes. In this paper, a method to design an intelligent suspension system with the objective to overcome the trade-off barrier using the smallest actuator is presented. An experts' based algorithm continuously monitors the road excitation in relation to the suspension travel and adapts accordingly the suspension system. It is shown that by applying genetic algorithm it is possible to optimally tune the system. However, the global optimum is hard to find due to the problem nonlinearity. A hybrid genetic algorithm that improves the probability of successfully finding the best design is presented. The simulation results show that the proposed intelligent system performs for - well known in the literature scenarios - better than others and remarkably this is achieved by reducing the actuator's size.