An uncertain multi-objective programming problem is a special type of mathematical multi-objective programming involving uncertain variables. This type of problem is important because there are several uncertain varia...An uncertain multi-objective programming problem is a special type of mathematical multi-objective programming involving uncertain variables. This type of problem is important because there are several uncertain variables in real-world problems.Therefore, research on the uncertain multi-objective programming problem is highly relevant, particularly those problems whose objective functions are correlated. In this paper, an approach that solves an uncertain multi-objective programming problem under the expected-variance value criterion is proposed. First, we define the basic framework of the approach and review concepts such as a Pareto efficient solution and expected-variance value criterion using an order relation between various uncertain variables.Second, the uncertain multi-objective problem is converted into an uncertain single-objective programming problem via a linear weighted method or ideal point method. Then the problem is transformed into a deterministic single objective programming problem under the expected-variance value criterion. Third, four lemmas and two theorems are proved to illustrate that the optimal solution of the deterministic single-objective programming problem is an efficient solution to the original uncertainty problem. Finally, two numerical examples are presented to validate the effectiveness of the proposed approach.展开更多
A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximatio...A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.展开更多
The suboptimal reliable guaranteed cost control (RGCC) with multi-criterion constraints is investigated for a class of uncertain continuous-time systems with sensor faults. A fault model in sensors, which considers ou...The suboptimal reliable guaranteed cost control (RGCC) with multi-criterion constraints is investigated for a class of uncertain continuous-time systems with sensor faults. A fault model in sensors, which considers outage or partial degradation of sensors, is adopted. The influence of the disturbance on the quadratic stability of the closed-loop systems is analyzed. The reliable state-feedback controller is developed by a linear matrix inequalities (LMIs) approach, to minimize the upper bound of a quadratic cost function under the conditions that all the closed-loop poles be placed in a specified disk, and that the prescribed level of H∞ disturbance attenuation and the upper bound constraints of control inputs’ magnitudes be guaranteed. Thus, with the above multi-criterion constraints, the resulting closed-loop system can provide satisfactory stability, transient property, a disturbance rejection level and minimized quadratic cost performance despite possible sensor faults.展开更多
多直流送端系统内某一直流线路发生闭锁故障后,可以通过对其他非故障直流进行紧急功率支援(emergency DC power support,EDCPS)控制来提升系统受扰后的稳定性。如何有效量化EDCPS对系统稳定性的改善效果,从而提高安稳控制的效率,目前缺...多直流送端系统内某一直流线路发生闭锁故障后,可以通过对其他非故障直流进行紧急功率支援(emergency DC power support,EDCPS)控制来提升系统受扰后的稳定性。如何有效量化EDCPS对系统稳定性的改善效果,从而提高安稳控制的效率,目前缺少相应的指标;现有研究也尚未解决如何利用EDCPS最大程度提升多直流综合送出能力的问题。应用扩展等面积法(extended equal area criterion,EEAC)定量分析了EDCPS提升多直流送端系统暂态稳定性的机理;以两送出直流系统为例,推导了基于EEAC的暂态稳定裕度η对EDCPS的灵敏度公式;在此基础上,提出一种在不增加相关设备投入的前提下利用系统中现有稳控措施并结合暂态稳定裕度灵敏度的多直流送出能力提升控制方法,通过计算备选直流的暂态稳定裕度灵敏度,可以完整反映系统在遭受大扰动后的动态特性以及定量评估EDCPS对系统受扰后稳定性的改善效果,避免过去仅凭运行人员经验选取支援直流的问题,提高计算效率;最后,选取西北电网实际算例对所提出的多直流送出能力提升控制方法有效性进行仿真验证,以期为从事交直流混联电力系统稳定性分析与控制的学者和工程师们提供有益的参考。展开更多
针对多目标突防组网雷达系统场景,为有效提高干扰效果以及突防成功率,编队航迹规划尤为重要。因此,首先构建航迹规划模型,从飞行器自身约束、航迹安全性、机间协调以及任务完成效果4个方面出发,结合多机伴随式编队及其所处环境特点,提...针对多目标突防组网雷达系统场景,为有效提高干扰效果以及突防成功率,编队航迹规划尤为重要。因此,首先构建航迹规划模型,从飞行器自身约束、航迹安全性、机间协调以及任务完成效果4个方面出发,结合多机伴随式编队及其所处环境特点,提出较为完备的航迹规划准则,形成一个新的整体目标函数;其次,为有效描述每架飞机的机动特性以及伴飞干扰机与目标飞机间的联系,提高算法搜索能力,提出基于多球面矢量(multi-spherical vector-based,MS)方法;为进一步提高算法的探索和开发能力,提出多面球矢量逐航迹点学习混合粒子群优化(multi-spherical vector-based hybrid particle swarm optimization with track point by track point learning,TLHPSO)算法,并将两者相结合,形成基于多面球矢量的逐航迹点学习混合粒子群优化(MS-based hybrid particle swarm optimization with track point by track point learning,MS-TLHPSO)航迹规划方法;最后,构建相应仿真场景进行验证。对比结果表明,MS方法以及TLHPSO优化算法在寻优能力上具有明显优势;同时,所提算法在不同初始场景下最优解的平均值均优于其他算法,充分说明所提算法能够在保证稳定性的前提下规划具有更高可信度的编队航迹。展开更多
基金supported by the National Natural Science Foundation of China(71601183 71571190)
文摘An uncertain multi-objective programming problem is a special type of mathematical multi-objective programming involving uncertain variables. This type of problem is important because there are several uncertain variables in real-world problems.Therefore, research on the uncertain multi-objective programming problem is highly relevant, particularly those problems whose objective functions are correlated. In this paper, an approach that solves an uncertain multi-objective programming problem under the expected-variance value criterion is proposed. First, we define the basic framework of the approach and review concepts such as a Pareto efficient solution and expected-variance value criterion using an order relation between various uncertain variables.Second, the uncertain multi-objective problem is converted into an uncertain single-objective programming problem via a linear weighted method or ideal point method. Then the problem is transformed into a deterministic single objective programming problem under the expected-variance value criterion. Third, four lemmas and two theorems are proved to illustrate that the optimal solution of the deterministic single-objective programming problem is an efficient solution to the original uncertainty problem. Finally, two numerical examples are presented to validate the effectiveness of the proposed approach.
基金Supported by National"863"Program of China (No.2006AA04Z127) .
文摘A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.
基金the National Natural Science Foundation of China (No. 60574082)the National Creative Research Groups Sci-ence Foundation of China (No. 60721062)the China Postdoc-toral Science Foundation (No. 20070411178)
文摘The suboptimal reliable guaranteed cost control (RGCC) with multi-criterion constraints is investigated for a class of uncertain continuous-time systems with sensor faults. A fault model in sensors, which considers outage or partial degradation of sensors, is adopted. The influence of the disturbance on the quadratic stability of the closed-loop systems is analyzed. The reliable state-feedback controller is developed by a linear matrix inequalities (LMIs) approach, to minimize the upper bound of a quadratic cost function under the conditions that all the closed-loop poles be placed in a specified disk, and that the prescribed level of H∞ disturbance attenuation and the upper bound constraints of control inputs’ magnitudes be guaranteed. Thus, with the above multi-criterion constraints, the resulting closed-loop system can provide satisfactory stability, transient property, a disturbance rejection level and minimized quadratic cost performance despite possible sensor faults.
文摘多直流送端系统内某一直流线路发生闭锁故障后,可以通过对其他非故障直流进行紧急功率支援(emergency DC power support,EDCPS)控制来提升系统受扰后的稳定性。如何有效量化EDCPS对系统稳定性的改善效果,从而提高安稳控制的效率,目前缺少相应的指标;现有研究也尚未解决如何利用EDCPS最大程度提升多直流综合送出能力的问题。应用扩展等面积法(extended equal area criterion,EEAC)定量分析了EDCPS提升多直流送端系统暂态稳定性的机理;以两送出直流系统为例,推导了基于EEAC的暂态稳定裕度η对EDCPS的灵敏度公式;在此基础上,提出一种在不增加相关设备投入的前提下利用系统中现有稳控措施并结合暂态稳定裕度灵敏度的多直流送出能力提升控制方法,通过计算备选直流的暂态稳定裕度灵敏度,可以完整反映系统在遭受大扰动后的动态特性以及定量评估EDCPS对系统受扰后稳定性的改善效果,避免过去仅凭运行人员经验选取支援直流的问题,提高计算效率;最后,选取西北电网实际算例对所提出的多直流送出能力提升控制方法有效性进行仿真验证,以期为从事交直流混联电力系统稳定性分析与控制的学者和工程师们提供有益的参考。
文摘针对多目标突防组网雷达系统场景,为有效提高干扰效果以及突防成功率,编队航迹规划尤为重要。因此,首先构建航迹规划模型,从飞行器自身约束、航迹安全性、机间协调以及任务完成效果4个方面出发,结合多机伴随式编队及其所处环境特点,提出较为完备的航迹规划准则,形成一个新的整体目标函数;其次,为有效描述每架飞机的机动特性以及伴飞干扰机与目标飞机间的联系,提高算法搜索能力,提出基于多球面矢量(multi-spherical vector-based,MS)方法;为进一步提高算法的探索和开发能力,提出多面球矢量逐航迹点学习混合粒子群优化(multi-spherical vector-based hybrid particle swarm optimization with track point by track point learning,TLHPSO)算法,并将两者相结合,形成基于多面球矢量的逐航迹点学习混合粒子群优化(MS-based hybrid particle swarm optimization with track point by track point learning,MS-TLHPSO)航迹规划方法;最后,构建相应仿真场景进行验证。对比结果表明,MS方法以及TLHPSO优化算法在寻优能力上具有明显优势;同时,所提算法在不同初始场景下最优解的平均值均优于其他算法,充分说明所提算法能够在保证稳定性的前提下规划具有更高可信度的编队航迹。
