针对汽车乘员约束系统高度非线性且难于求解最优值的特点,提出全局敏感性分析结合混合元模型的优化方法,通过蒙特卡罗模拟在整个设计空间内采样,以元模型代替仿真模型来完成设计参数的敏感性分析,并将分析获得的信息用于混合元模型优化(...针对汽车乘员约束系统高度非线性且难于求解最优值的特点,提出全局敏感性分析结合混合元模型的优化方法,通过蒙特卡罗模拟在整个设计空间内采样,以元模型代替仿真模型来完成设计参数的敏感性分析,并将分析获得的信息用于混合元模型优化(hybrid and adaptive metamodeling method,HAM),将二阶多项式响应面、Kriging模型、径向基函数三种元模型有机结合,自适应选择最佳的元模型进行寻优.搜索过程中元模型不断更新与重建,逐渐提高关键区域的精度,从而快速寻找到全局最优解.对某工程实例的优化结果表明该方法是有效的.展开更多
An inverted pendulum is a sensitive system of highly coupled parameters, in laboratories, it is popular for modelling nonlinear systems such as mechanisms and control systems, and also for optimizing programmes before...An inverted pendulum is a sensitive system of highly coupled parameters, in laboratories, it is popular for modelling nonlinear systems such as mechanisms and control systems, and also for optimizing programmes before those programmes are applied in real situations. This study aims to find the optimum input setting for a double inverted pendulum（DIP）, which requires an appropriate input to be able to stand and to achieve robust stability even when the system model is unknown. Such a DIP input could be widely applied in engineering fields for optimizing unknown systems with a limited budget. Previous studies have used various mathematical approaches to optimize settings for DIP, then have designed control algorithms or physical mathematical models.This study did not adopt a mathematical approach for the DIP controller because our DIP has five input parameters within its nondeterministic system model. This paper proposes a novel algorithm, named Uni Neuro, that integrates neural networks（NNs） and a uniform design（UD） in a model formed by input and response to the experimental data（metamodel）. We employed a hybrid UD multiobjective genetic algorithm（HUDMOGA） for obtaining the optimized setting input parameters. The UD was also embedded in the HUDMOGA for enriching the solution set, whereas each chromosome used for crossover, mutation, and generation of the UD was determined through a selection procedure and derived individually. Subsequently, we combined the Euclidean distance and Pareto front to improve the performance of the algorithm. Finally, DIP equipment was used to confirm the settings. The proposed algorithm can produce 9 alternative configured input parameter values to swing-up then standing in robust stability of the DIP from only 25 training data items and 20 optimized simulation results. In comparison to the full factorial design, this design can save considerable experiment time because the metamodel can be formed by only 25 experiments using the UD. Furthermore, the proposed algorithm can be applied to nonlinear systems with multiple constraints.展开更多
文摘针对汽车乘员约束系统高度非线性且难于求解最优值的特点,提出全局敏感性分析结合混合元模型的优化方法,通过蒙特卡罗模拟在整个设计空间内采样,以元模型代替仿真模型来完成设计参数的敏感性分析,并将分析获得的信息用于混合元模型优化(hybrid and adaptive metamodeling method,HAM),将二阶多项式响应面、Kriging模型、径向基函数三种元模型有机结合,自适应选择最佳的元模型进行寻优.搜索过程中元模型不断更新与重建,逐渐提高关键区域的精度,从而快速寻找到全局最优解.对某工程实例的优化结果表明该方法是有效的.
基金supported by Indonesian Government(No.BPPLN DIKTI 3+1)
文摘An inverted pendulum is a sensitive system of highly coupled parameters, in laboratories, it is popular for modelling nonlinear systems such as mechanisms and control systems, and also for optimizing programmes before those programmes are applied in real situations. This study aims to find the optimum input setting for a double inverted pendulum（DIP）, which requires an appropriate input to be able to stand and to achieve robust stability even when the system model is unknown. Such a DIP input could be widely applied in engineering fields for optimizing unknown systems with a limited budget. Previous studies have used various mathematical approaches to optimize settings for DIP, then have designed control algorithms or physical mathematical models.This study did not adopt a mathematical approach for the DIP controller because our DIP has five input parameters within its nondeterministic system model. This paper proposes a novel algorithm, named Uni Neuro, that integrates neural networks（NNs） and a uniform design（UD） in a model formed by input and response to the experimental data（metamodel）. We employed a hybrid UD multiobjective genetic algorithm（HUDMOGA） for obtaining the optimized setting input parameters. The UD was also embedded in the HUDMOGA for enriching the solution set, whereas each chromosome used for crossover, mutation, and generation of the UD was determined through a selection procedure and derived individually. Subsequently, we combined the Euclidean distance and Pareto front to improve the performance of the algorithm. Finally, DIP equipment was used to confirm the settings. The proposed algorithm can produce 9 alternative configured input parameter values to swing-up then standing in robust stability of the DIP from only 25 training data items and 20 optimized simulation results. In comparison to the full factorial design, this design can save considerable experiment time because the metamodel can be formed by only 25 experiments using the UD. Furthermore, the proposed algorithm can be applied to nonlinear systems with multiple constraints.
