为了提高涡激式微型风能采集装置能量回收功率,文章基于同步电荷提取(SECE)电路,利用MSP430超低功耗单片机系统,设计了硬件电路与控制算法.对压电双晶片的两端电压进行峰值检测,同时产生脉冲信号,控制电路中MOS管的导通与关断,缩短压电片积累电荷的能量损耗,提升电路的回收功率.实验结果表明,在风速为14,16 m/s和18 m/s的条件下,与经典电路相比,基于SECE电路的涡激式微型风能采集装置的能量回收功率分别提高了75.3％,52.2％和47.8％.

针对工业外场环境下,传统电池供能方式存在污染环境及不易更换的问题,通过压电陶瓷将微型风能转换成电能后,直接为无线传感节点等微型电子设备供电,可以缓解这一问题.针对经典电路回收微型风能功率较低的问题,提出了一种无源自供能接口电路,可以更高效地采集微型风能.对电路进行了理论分析和Multisim软件仿真,仿真结果表明:设计的电路回收微型风能是经典电路的4倍左右.

由于体积大、污染环境、需要定期更换,传统电池供能方式已不能适应当前外场工作的需求.涡激振动的微型风能采集装置将风能转换成电能,能够对无线传感节点等微型电子设备直接供电.基于经典Buck-Boost电路,提出了一种适用于涡激振动微型风能采集的能量接口电路.通过理论与仿真分析,所设计的能量接口电路存在最优占空比,及其对应的最大功率点.基于LabVIEW平台设计了控制程序,实验结果表明,所设计的电路与程序能够对占空比进行自动寻优,保持微型风能采集装置以最大功率输出.

Micro wind harvesting system with piezoelectric structure is an attractive method to power micro electric devices in outdoor field work. Interface circuit is one of the keys to improve the efficiency of harvested energy. A SECE interface circuit is design with microcontroller, which monitors the voltage and generates pulse signal. A simulation is performed in Multisim software to validate the proposed circuit. Results show that the maximum output power of classic interface circuit is 128.118μW at 110KΩ, and it increases to 390μW with proposed SECE interface circuit. It indicates that the efficiency of harvested energy is improved by 200%.

为了降低压电陶瓷驱动器的迟滞非线性,提出了改进型的Maxwell-slip模型并引入自适应控制,使压电驱动器在宽频带下有良好的迟滞补偿效果.在经典Maxwell-slip模型中,输出力与输入位移的关系会出现迟滞现象,表现为平行四边形,与压电陶瓷驱动器的迟滞特性接近.由于每一单元滑块的最大静摩擦力与弹簧弹性系数成比例关系,若弹簧系数取定值时,每一个单元的最大静摩擦力在系统实时控制中是不变的,因此可以采用自适应控制算法对输出信号权值进行更新,从而更精确地补偿压电陶瓷驱动器.为了验证该模型,搭建了悬臂梁结构压电实验平台,运用该迟滞模型进行迟滞补偿控制,实验结果表明,对于Maxwell-slip模型自适应控制,在0.1～20 Hz宽频带下的均方根误差(RMSE)和绝对平均误差(MAE)均有减小.其中,在0.1 Hz下无前馈补偿控制的RMSE为0.037 5 μm,而通过自适应控制可以将压电微定位平台的RMSE降低到0.012 4μm以内.与经典模型相比,所提出的Maxwell-slip模型自适应控制具有在宽频带内进行精密定位的优点.

Classic Preisach model can precisely describe the hysteresis of piezoelectric stack actuators, but its model identification is relatively complicated. Neural network is easy to be identified with available training algorithm, but it cannot directly describe the multi-valued mapping of hysteresis. A neural-Preisach model was proposed for modeling and control of piezoelectric stack actuators. The neural-Preisach model inherits the advantages of Preisach model and neural network, which can describe the hysteresis and update parameters by training algorithm. A feedforward controller was designed with the inverse neural-Preisach model, and then experiments of tracking control were performed to validate the effectiveness of the neural-Preisach model. The maximal error, in case of feedforward and PID controller, is reduced by 83.97%, comparing with the case without control. This indicates that control accuracy with hysteresis compensation is greatly improved compared to that without hysteresis compensation.