摘要
Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimization methods capable of providing the best trade-off designs compromising both criteria simultaneously. Although many studies have been performed on multi-objective optimization of vehicle suspension system, only a few of them have used probabilistic approaches considering effects of uncertainties in the design. However, it has been proved that optimum point obtained from deterministic optimization without taking into account the effects of uncertainties may lead to high-risk points instead of optimum ones. In this work, reliability-based robust multi-objective optimization of a 5 degree of freedom （5-DOF） vehicle suspension system is performed using method of non-dominated sorting genetic algorithm-II （NSGA-II） in conjunction with Monte Carlo simulation （MCS） to obtain best designs considering both comfort and handling. Road profile is modeled as a random function using power spectral density （PSD） which is in better accordance with reality. To accommodate the robust approach, the variance of all objective functions is also considered to be minimized. Also, to take into account the reliability criterion, a reliability-based constraint is considered in the optimization. A deterministic optimization has also been performed to compare the results with probabilistic study and some other deterministic studies in the literature. In addition, sensitivity analysis has been performed to reveal the effects of different design variables on objective functions. To introduce the best trade-off points from the obtained Pareto fronts, TOPSIS method has been employed. Results show that optimum design point obtained from probabilistic optimization in this work provides better performance while demonstrating very good reliability and robustness. However, other optimum points from deterministic optimizations violate the regarded constraints in the presence of uncertainties.
Ride and handling are two paramount factors in design and development of vehicle suspension systems.Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimization methods capable of providing the best trade-off designs compromising both criteria simultaneously.Although many studies have been performed on multi-objective optimization of vehicle suspension system,only a few of them have used probabilistic approaches considering effects of uncertainties in the design.However,it has been proved that optimum point obtained from deterministic optimization without taking into account the effects of uncertainties may lead to high-risk points instead of optimum ones.In this work,reliability-based robust multi-objective optimization of a 5 degree of freedom(5-DOF) vehicle suspension system is performed using method of non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ) in conjunction with Monte Carlo simulation(MCS) to obtain best designs considering both comfort and handling.Road profile is modeled as a random function using power spectral density(PSD) which is in better accordance with reality.To accommodate the robust approach,the variance of all objective functions is also considered to be minimized.Also,to take into account the reliability criterion,a reliability-based constraint is considered in the optimization.A deterministic optimization has also been performed to compare the results with probabilistic study and some other deterministic studies in the literature.In addition,sensitivity analysis has been performed to reveal the effects of different design variables on objective functions.To introduce the best trade-off points from the obtained Pareto fronts,TOPSIS method has been employed.Results show that optimum design point obtained from probabilistic optimization in this work provides better performance while demonstrating very good reliability and robustness.However,other optimum points from deterministic optimizations violate the regarded constraints in the presence of uncertainties.
