Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employe...Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.展开更多
在两级式AC-DC变换器中,前级功率因数校正(power factor correction,PFC)固有的瞬时功率波动特性会造成母线电压存在二倍频纹波,影响后级CLLLC谐振变换器的输出电压质量。针对以上问题,该文提出了基于二阶广义积分(second order general...在两级式AC-DC变换器中,前级功率因数校正(power factor correction,PFC)固有的瞬时功率波动特性会造成母线电压存在二倍频纹波,影响后级CLLLC谐振变换器的输出电压质量。针对以上问题,该文提出了基于二阶广义积分(second order generalized integral,SOGI)的可变增益母线电压纹波前馈控制方法。采用SOGI提取母线电压纹波信息,基于品质因数Q与电压增益的关系和母线电压纹波对归一化频率的影响,解析了母线电压纹波对CLLLC谐振变换器输出电压的影响机理,得到Q值与前馈增益系数Ka的关系,采用仿真寻优加数据拟合的方法得到前馈可变增益系数曲线。仿真和实验结果表明,相比于无前馈控制,所提控制方法对CLLLC谐振变换器的输出电压纹波具有较好的抑制效果,输出电压纹波降低了72%,验证了所提算法的有效性。展开更多
基金Project(60634020) supported by the National Natural Science Foundation of China
文摘Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.
