This article introduces a singleton type-1 fuzzy logic system(T1-SFLS) controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. T...This article introduces a singleton type-1 fuzzy logic system(T1-SFLS) controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO(Wind Driven Optimization) algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-Ⅲ mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.展开更多
鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种...鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种基于数据驱动不确定集的微电网两阶段鲁棒优化调度方法。首先,通过风电历史数据构建条件正态Copula(conditional normal copula,CNC)模型,再将日前风电预测值输入CNC模型生成次日风电功率样本。然后,通过支持向量聚类(support vector clustering,SVC)和维度分解构建考虑风电时间相关性的数据驱动不确定集。该不确定集可更为准确地刻画风电不确定性,并将风电数据中的异常值排除在外,从而在降低鲁棒优化保守性的同时具备异常值抵抗性。其次,提出了基于上述不确定集的两阶段鲁棒优化调度模型,并采用列约束生成(column and constraint generation,C&CG)算法求解。最后通过仿真证明了相较传统不确定集,本文构建的不确定集保守性更低,同时对风电数据异常值具有良好的抵抗性。展开更多
文摘This article introduces a singleton type-1 fuzzy logic system(T1-SFLS) controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO(Wind Driven Optimization) algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-Ⅲ mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.
文摘鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种基于数据驱动不确定集的微电网两阶段鲁棒优化调度方法。首先,通过风电历史数据构建条件正态Copula(conditional normal copula,CNC)模型,再将日前风电预测值输入CNC模型生成次日风电功率样本。然后,通过支持向量聚类(support vector clustering,SVC)和维度分解构建考虑风电时间相关性的数据驱动不确定集。该不确定集可更为准确地刻画风电不确定性,并将风电数据中的异常值排除在外,从而在降低鲁棒优化保守性的同时具备异常值抵抗性。其次,提出了基于上述不确定集的两阶段鲁棒优化调度模型,并采用列约束生成(column and constraint generation,C&CG)算法求解。最后通过仿真证明了相较传统不确定集,本文构建的不确定集保守性更低,同时对风电数据异常值具有良好的抵抗性。
