鉴于蒸汽压缩式制冷机组蒸发温度T_(e)与过热度D_(sh)的控制回路之间存在强耦合及大惯性、非线性和时延等特性。提出了一种蒸发温度与过热度的前馈解耦PID控制策略,且设计出改进多目标人工鱼群算法(Modified Multi-objective Artificial...鉴于蒸汽压缩式制冷机组蒸发温度T_(e)与过热度D_(sh)的控制回路之间存在强耦合及大惯性、非线性和时延等特性。提出了一种蒸发温度与过热度的前馈解耦PID控制策略,且设计出改进多目标人工鱼群算法(Modified Multi-objective Artificial Fish Swarm Algorithm,MMOAFSA)对相应的PID控制器参数进行整定,以提升T_(e)与D_(sh)的调节质量。首先,对两个控制环路:电子膨胀阀开度O_(EEV)—蒸发温度T_(e)和压缩机驱动电机的供电频率f—过热度D_(sh),通过前馈补偿解耦方式来消除这两个控制回路之间的耦合效应。其次,对基本型单目标人工鱼群算法的视野V和步长S进行指数递减变化,构建改进单目标人工鱼群算法(Modified Single Objective Artificial Fish Swarm Algorithm,MSOAFSA)。再将多目标优化的混沌局部搜索策略引入MSOAFSA,设计了MMOAFSA。考虑绝对积分时间误差(Integrated Time Absolute Error,ITAE)、调节时间tc和稳态误差绝对值Ess,将min(ITAE,tc,Ess)作为MMOAFSA的多目标适应度函数,并应用该MMOAFSA对两个控制器的6个参数(KP_(1),KI_(1),KD_(1),KP_(2),KI_(2),KD_(2))进行多目标寻优,获取了相应的Pareto最优解。最后,借助MATLAB工具,对VCRU双参数前馈解耦PID控制系统(Two-Parameter Feedforward Decoupling PID Control System for VCRU,VCRU-TPFDPIDCS)组态与数值模拟。结果表明:该控制策略能够消除控制回路之间的耦合效应,同时MMOAFSA对两个控制器6个参数的自适应整定是可行的,且对T_(e)与D_(sh)的调节质量也明显优于传统的PID调节方式。展开更多
基于改进人工鱼群算法（Modified Artificial Fish Swarm Algorithm,MAFSA）对电力系统进行最优潮流计算的方法；在整合动态调整罚函数方式，将最优潮流转化为一无约束求系统发电费用最小的极值问题，为了提高算法收敛精度，对人工鱼群...基于改进人工鱼群算法（Modified Artificial Fish Swarm Algorithm,MAFSA）对电力系统进行最优潮流计算的方法；在整合动态调整罚函数方式，将最优潮流转化为一无约束求系统发电费用最小的极值问题，为了提高算法收敛精度，对人工鱼群中参数一补偿和拥挤度因子进行改进。仿真计算结果表明算法有效性。展开更多
Aiming at the design problem of aviation swarm combat course of action(COA),considering the influence of stochastic parameters in the causal relationship model and optimization problem model,according to the dynamic i...Aiming at the design problem of aviation swarm combat course of action(COA),considering the influence of stochastic parameters in the causal relationship model and optimization problem model,according to the dynamic influence net(DIN)theory,stochastic simulation technique,feedforward neural network(FNN)function approximation technique and multi-objective artificial fish school algorithm(MOAFSA),this paper proposed a COA optimized method based on DIN and multi-objective stochastic chance constraint optimization for aviation swarm combat.First,on the basis of establishing the overall framework of the model and defining the elements of causal relationship modeling,the static and dynamic causal relationship modeling and optimization problem modeling were carried out respectively.Second,the probability propagation mechanism of DIN was established,which mainly included two aspects,i.e.,the overall process and the specific algorithm.Then,input and output data were generated based on stochastic simulation.According to these data,FNN was adopted for function approximation,and MOAFSA was adopted for iterative optimization.Finally,the rationality of the model,and the effectiveness and superiority of the algorithm were verified through multiple sets of simulation cases.展开更多
文摘鉴于蒸汽压缩式制冷机组蒸发温度T_(e)与过热度D_(sh)的控制回路之间存在强耦合及大惯性、非线性和时延等特性。提出了一种蒸发温度与过热度的前馈解耦PID控制策略,且设计出改进多目标人工鱼群算法(Modified Multi-objective Artificial Fish Swarm Algorithm,MMOAFSA)对相应的PID控制器参数进行整定,以提升T_(e)与D_(sh)的调节质量。首先,对两个控制环路:电子膨胀阀开度O_(EEV)—蒸发温度T_(e)和压缩机驱动电机的供电频率f—过热度D_(sh),通过前馈补偿解耦方式来消除这两个控制回路之间的耦合效应。其次,对基本型单目标人工鱼群算法的视野V和步长S进行指数递减变化,构建改进单目标人工鱼群算法(Modified Single Objective Artificial Fish Swarm Algorithm,MSOAFSA)。再将多目标优化的混沌局部搜索策略引入MSOAFSA,设计了MMOAFSA。考虑绝对积分时间误差(Integrated Time Absolute Error,ITAE)、调节时间tc和稳态误差绝对值Ess,将min(ITAE,tc,Ess)作为MMOAFSA的多目标适应度函数,并应用该MMOAFSA对两个控制器的6个参数(KP_(1),KI_(1),KD_(1),KP_(2),KI_(2),KD_(2))进行多目标寻优,获取了相应的Pareto最优解。最后,借助MATLAB工具,对VCRU双参数前馈解耦PID控制系统(Two-Parameter Feedforward Decoupling PID Control System for VCRU,VCRU-TPFDPIDCS)组态与数值模拟。结果表明:该控制策略能够消除控制回路之间的耦合效应,同时MMOAFSA对两个控制器6个参数的自适应整定是可行的,且对T_(e)与D_(sh)的调节质量也明显优于传统的PID调节方式。
文摘基于改进人工鱼群算法（Modified Artificial Fish Swarm Algorithm,MAFSA）对电力系统进行最优潮流计算的方法；在整合动态调整罚函数方式，将最优潮流转化为一无约束求系统发电费用最小的极值问题，为了提高算法收敛精度，对人工鱼群中参数一补偿和拥挤度因子进行改进。仿真计算结果表明算法有效性。
基金co-supported by Natural Science Foundation of Shaanxi(2023-JC-QN-0728)Postdoctoral Science Foundation of China(2021M693942)。
文摘Aiming at the design problem of aviation swarm combat course of action(COA),considering the influence of stochastic parameters in the causal relationship model and optimization problem model,according to the dynamic influence net(DIN)theory,stochastic simulation technique,feedforward neural network(FNN)function approximation technique and multi-objective artificial fish school algorithm(MOAFSA),this paper proposed a COA optimized method based on DIN and multi-objective stochastic chance constraint optimization for aviation swarm combat.First,on the basis of establishing the overall framework of the model and defining the elements of causal relationship modeling,the static and dynamic causal relationship modeling and optimization problem modeling were carried out respectively.Second,the probability propagation mechanism of DIN was established,which mainly included two aspects,i.e.,the overall process and the specific algorithm.Then,input and output data were generated based on stochastic simulation.According to these data,FNN was adopted for function approximation,and MOAFSA was adopted for iterative optimization.Finally,the rationality of the model,and the effectiveness and superiority of the algorithm were verified through multiple sets of simulation cases.
