The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible ...The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.展开更多
为了实现永磁同步直线电机PMSLM(permanent magnet synchronous linear motor)高精度的多电气参数在线辨识,提出了一种基于双模型的递推最小二乘电气参数在线辨识算法。首先,根据电机的dq轴电压方程分别建立了辨识定子电阻、永磁体磁链...为了实现永磁同步直线电机PMSLM(permanent magnet synchronous linear motor)高精度的多电气参数在线辨识,提出了一种基于双模型的递推最小二乘电气参数在线辨识算法。首先,根据电机的dq轴电压方程分别建立了辨识定子电阻、永磁体磁链的模型1和辨识q轴电感、d轴电感的模型2,并将2个辨识模型循环结合。其次,基于上述双模型结构,采用递推最小二乘算法实现电气参数在线辨识,并针对PMSLM运行时存在大量动态过程的特性,提出一种具有饱和特性的分段变遗忘因子;然后,对功率开关非理想因素导致的误差电压进行补偿,进一步提高了辨识的精准度;最后,仿真和实验结果证明了该辨识算法的有效性,且具有收敛速度快、辨识结果精度高、多工况适用等优点。展开更多
为了有效改善燃料电池混合动力系统的能耗,减少燃料电池性能衰减,保持辅助动力源的荷电状态(state of charge,SOC),提出一种基于遗忘因子递推最小二乘算法(forgetting factor recursive least square,FFRLS)的在线辨识方法和极小值原理...为了有效改善燃料电池混合动力系统的能耗,减少燃料电池性能衰减,保持辅助动力源的荷电状态(state of charge,SOC),提出一种基于遗忘因子递推最小二乘算法(forgetting factor recursive least square,FFRLS)的在线辨识方法和极小值原理的综合能量管理方法。该方法能根据在线辨识的结果和直流母线需求功率,完成对主动力源及辅助动力源的功率分配工作,并与基于离线辨识的算法结果以及等效氢耗最小能量管理方法(equivalent consumption minimization strategy,ECMS)进行对比分析。结果表明,该方法对等效氢耗的优化比离线以及ECMS的效果分别提升了6.33%和4.35%,对燃料电池性能衰减则分别优化了4.72%和6.98%,并能更好地维持辅助动力源的SOC。展开更多
文摘The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.
文摘为了实现永磁同步直线电机PMSLM(permanent magnet synchronous linear motor)高精度的多电气参数在线辨识,提出了一种基于双模型的递推最小二乘电气参数在线辨识算法。首先,根据电机的dq轴电压方程分别建立了辨识定子电阻、永磁体磁链的模型1和辨识q轴电感、d轴电感的模型2,并将2个辨识模型循环结合。其次,基于上述双模型结构,采用递推最小二乘算法实现电气参数在线辨识,并针对PMSLM运行时存在大量动态过程的特性,提出一种具有饱和特性的分段变遗忘因子;然后,对功率开关非理想因素导致的误差电压进行补偿,进一步提高了辨识的精准度;最后,仿真和实验结果证明了该辨识算法的有效性,且具有收敛速度快、辨识结果精度高、多工况适用等优点。
文摘为了有效改善燃料电池混合动力系统的能耗,减少燃料电池性能衰减,保持辅助动力源的荷电状态(state of charge,SOC),提出一种基于遗忘因子递推最小二乘算法(forgetting factor recursive least square,FFRLS)的在线辨识方法和极小值原理的综合能量管理方法。该方法能根据在线辨识的结果和直流母线需求功率,完成对主动力源及辅助动力源的功率分配工作,并与基于离线辨识的算法结果以及等效氢耗最小能量管理方法(equivalent consumption minimization strategy,ECMS)进行对比分析。结果表明,该方法对等效氢耗的优化比离线以及ECMS的效果分别提升了6.33%和4.35%,对燃料电池性能衰减则分别优化了4.72%和6.98%,并能更好地维持辅助动力源的SOC。
