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
As for the application of electronic fuel injection （EFI） system to small gasoline generator set, mechanical speed controller cannot be coupled with EFI system and has the shortcomings of lagged regulation and poor ...As for the application of electronic fuel injection （EFI） system to small gasoline generator set, mechanical speed controller cannot be coupled with EFI system and has the shortcomings of lagged regulation and poor accuracy, a feed-forward control strategy based on load combined with proportional-integral-differential （PID） control strategy was proposed, and a digital speed controller applied to the electrical control system was designed. The detailed control strategy of the controller was intro- duced. The hardware design for the controller and the key circuits of motor driving, current sampling and angular signal captu- ring were given, and software architecture was discussed. Combined with a gasoline generator set mounted with EFI system, the controller parameters were tuned and optimized empirically by hardware in loop and bench test methods. Test results show that the speed deviation of generator set is low and the control system is stable in steady state; In transient state the control system responses quickly, has high stability under mutation loads especially when suddenly apply and remove 100% load, the speed deviation is within 8% of reference speed and the transient time is less than 5 s, satisfying the ISO standard.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As for the application of electronic fuel injection （EFI） system to small gasoline generator set, mechanical speed controller cannot be coupled with EFI system and has the shortcomings of lagged regulation and poor accuracy, a feed-forward control strategy based on load combined with proportional-integral-differential （PID） control strategy was proposed, and a digital speed controller applied to the electrical control system was designed. The detailed control strategy of the controller was intro- duced. The hardware design for the controller and the key circuits of motor driving, current sampling and angular signal captu- ring were given, and software architecture was discussed. Combined with a gasoline generator set mounted with EFI system, the controller parameters were tuned and optimized empirically by hardware in loop and bench test methods. Test results show that the speed deviation of generator set is low and the control system is stable in steady state; In transient state the control system responses quickly, has high stability under mutation loads especially when suddenly apply and remove 100% load, the speed deviation is within 8% of reference speed and the transient time is less than 5 s, satisfying the ISO standard.
