This work proposes an event-triggered adaptive control approach for a class of uncertain nonlinear systems under irregular constraints.Unlike the constraints considered in most existing papers,here the external irregu...This work proposes an event-triggered adaptive control approach for a class of uncertain nonlinear systems under irregular constraints.Unlike the constraints considered in most existing papers,here the external irregular constraints are considered and a constraints switching mechanism(CSM)is introduced to circumvent the difficulties arising from irregular output constraints.Based on the CSM,a new class of generalized barrier functions are constructed,which allows the control results to be independent of the maximum and minimum values(MMVs)of constraints and thus extends the existing results.Finally,we proposed a novel dynamic constraint-driven event-triggered strategy(DCDETS),under which the stress on signal transmission is reduced greatly and no constraints are violated by making a dynamic trade-off among system state,external constraints,and inter-execution intervals.It is proved that the system output is driven to close to the reference trajectory and the semi-global stability is guaranteed under the proposed control scheme,regardless of the external irregular output constraints.Simulation also verifies the effectiveness and benefits of the proposed method.展开更多
This paper presents a methodology driven by database constraints for designing and developing(database)software applications.Much needed and with excellent results,this paradigm guarantees the highest possible quality...This paper presents a methodology driven by database constraints for designing and developing(database)software applications.Much needed and with excellent results,this paradigm guarantees the highest possible quality of the managed data.The proposed methodology is illustrated with an easy to understand,yet complex medium-sized genealogy software application driven by more than 200 database constraints,which fully meets such expectations.展开更多
微能源网是多能流耦合的微电网系统,为优化其设备容量配置,针对内部分布式可再生能源和负荷的不确定性,提出了综合范数约束下场景概率驱动的微能源网分布鲁棒规划方法。基于风-光出力和电-热-氢负荷的历史数据聚类提取典型场景,并构建基...微能源网是多能流耦合的微电网系统,为优化其设备容量配置,针对内部分布式可再生能源和负荷的不确定性,提出了综合范数约束下场景概率驱动的微能源网分布鲁棒规划方法。基于风-光出力和电-热-氢负荷的历史数据聚类提取典型场景,并构建基于1-范数和∞-范数约束的场景概率分布不确定集以描述源-荷不确定性。以年综合成本最低为目标建立min-max-min三层分布鲁棒规划模型,采用列与约束生成(column and constraint generation, C&CG)算法求解。仿真结果表明,引入所提场景概率不确定集和分布鲁棒模型后,系统年综合成本降低1.33%,且模型在经济性和鲁棒性的平衡方面相比传统随机优化和鲁棒优化更有优势,可在更短的迭代次数内降低年综合成本。展开更多
场景生成技术通过模拟多种典型场景的随机性和多样性,为解决历史数据样本有限和极端场景难以覆盖的问题提供了重要数据支撑。该文提出一种基于特征约束与目标驱动的WGAN-GP(Wasserstein GAN with gradient penalty)光伏多场景生成方法...场景生成技术通过模拟多种典型场景的随机性和多样性,为解决历史数据样本有限和极端场景难以覆盖的问题提供了重要数据支撑。该文提出一种基于特征约束与目标驱动的WGAN-GP(Wasserstein GAN with gradient penalty)光伏多场景生成方法。该方法通过引入Wasserstein距离及梯度惩罚,提高了生成器的稳定性与生成样本的多样性;在此基础上,提取晴天、阴雨天和多云天3类典型场景的关键特征,明确生成目标,并在生成器的输出阶段嵌入动态门函数,动态划定白天与夜间的分界点,确保生成的夜间输出为零,白天的辐照度变化符合实际物理规律;此外,采用加权采样优化策略,通过动态调整样本权重和选中概率,进一步强化对关键特性样本的学习,使生成器能够更准确地捕捉目标特性,从而显著提升稀缺场景的生成效果。算例结果表明,该方法能够精准捕捉不同天气场景的关键特征,生成样本在目标特性匹配及物理合理性方面表现良好,为光伏场景生成提供了一种可靠的解决方案。展开更多
R-DSP(Radar Digital Signal Processor)芯片中BSU(Branch Shift Unit)运算部件具有较大的设计规模和复杂度,传统Verilog验证平台难以满足其验证需求问题。针对该问题,文中采用UVM(Universal Verification Methodology)方法对BSU运算部...R-DSP(Radar Digital Signal Processor)芯片中BSU(Branch Shift Unit)运算部件具有较大的设计规模和复杂度,传统Verilog验证平台难以满足其验证需求问题。针对该问题,文中采用UVM(Universal Verification Methodology)方法对BSU运算部件进行功能验证。搭建基于SystemVerilog语言实现的UVM验证平台,使用定向测试和带约束的随机测试进行验证,并采用覆盖率驱动的方法指导测试用例的生成,以充分覆盖BSU运算部件的各个功能和代码路径。经过多轮测试激励验证,代码覆盖率接近100%,完成了对BSU运算部件的功能验证。所提方法为R-DSP芯片中的ALU(Arithmetic Logic Unit)、AGU(Address Generation Unit)、MU(Multiplication Unit)等运算部件的验证工作提供了参考和借鉴。展开更多
基金supported in part by the National Key Research and Development Program of China(2023YFA1011803)the National Natural Science Foundation of China(62273064,61933012,62250710167,61860206008,62203078)the Central University Project(2021CDJCGJ002,2022CDJKYJH019,2022CDJKYJH051)。
文摘This work proposes an event-triggered adaptive control approach for a class of uncertain nonlinear systems under irregular constraints.Unlike the constraints considered in most existing papers,here the external irregular constraints are considered and a constraints switching mechanism(CSM)is introduced to circumvent the difficulties arising from irregular output constraints.Based on the CSM,a new class of generalized barrier functions are constructed,which allows the control results to be independent of the maximum and minimum values(MMVs)of constraints and thus extends the existing results.Finally,we proposed a novel dynamic constraint-driven event-triggered strategy(DCDETS),under which the stress on signal transmission is reduced greatly and no constraints are violated by making a dynamic trade-off among system state,external constraints,and inter-execution intervals.It is proved that the system output is driven to close to the reference trajectory and the semi-global stability is guaranteed under the proposed control scheme,regardless of the external irregular output constraints.Simulation also verifies the effectiveness and benefits of the proposed method.
文摘This paper presents a methodology driven by database constraints for designing and developing(database)software applications.Much needed and with excellent results,this paradigm guarantees the highest possible quality of the managed data.The proposed methodology is illustrated with an easy to understand,yet complex medium-sized genealogy software application driven by more than 200 database constraints,which fully meets such expectations.
文摘微能源网是多能流耦合的微电网系统,为优化其设备容量配置,针对内部分布式可再生能源和负荷的不确定性,提出了综合范数约束下场景概率驱动的微能源网分布鲁棒规划方法。基于风-光出力和电-热-氢负荷的历史数据聚类提取典型场景,并构建基于1-范数和∞-范数约束的场景概率分布不确定集以描述源-荷不确定性。以年综合成本最低为目标建立min-max-min三层分布鲁棒规划模型,采用列与约束生成(column and constraint generation, C&CG)算法求解。仿真结果表明,引入所提场景概率不确定集和分布鲁棒模型后,系统年综合成本降低1.33%,且模型在经济性和鲁棒性的平衡方面相比传统随机优化和鲁棒优化更有优势,可在更短的迭代次数内降低年综合成本。
文摘场景生成技术通过模拟多种典型场景的随机性和多样性,为解决历史数据样本有限和极端场景难以覆盖的问题提供了重要数据支撑。该文提出一种基于特征约束与目标驱动的WGAN-GP(Wasserstein GAN with gradient penalty)光伏多场景生成方法。该方法通过引入Wasserstein距离及梯度惩罚,提高了生成器的稳定性与生成样本的多样性;在此基础上,提取晴天、阴雨天和多云天3类典型场景的关键特征,明确生成目标,并在生成器的输出阶段嵌入动态门函数,动态划定白天与夜间的分界点,确保生成的夜间输出为零,白天的辐照度变化符合实际物理规律;此外,采用加权采样优化策略,通过动态调整样本权重和选中概率,进一步强化对关键特性样本的学习,使生成器能够更准确地捕捉目标特性,从而显著提升稀缺场景的生成效果。算例结果表明,该方法能够精准捕捉不同天气场景的关键特征,生成样本在目标特性匹配及物理合理性方面表现良好,为光伏场景生成提供了一种可靠的解决方案。
