Updating parameters according to the driving rate of input, the rate-dependent Prandtl-Ishlinskii （PI） model is widely used in hysteresis modeling and compensation. In order to improve the modeling accuracy, two PI ...Updating parameters according to the driving rate of input, the rate-dependent Prandtl-Ishlinskii （PI） model is widely used in hysteresis modeling and compensation. In order to improve the modeling accuracy, two PI models identified at low and high driving rates separately are incorporated through a combination law. For the piezo- driven flexure-based mechanism, the very low damping ratio makes it easy to excite the structural vibration. As a re- suit, the measured hysteresis loop is greatly distorted and the modeling accuracy of the identified P1 model is signifi- cantly affected. In this paper, a novel time-efficient parameter identification method which utilizes the superimposed sinusoidal signals as the control input is proposed. This method effectively avoids the excitation of the structural vibra- tion. In addition, as the driving rate of the superimposed sinusoidal signals covers a wide range, all the coefficients required for modeling the rate-dependence can be identified through only one set of experimental data. Hysteresis modeling and trajectory tracking experiments were performed on a 2-DOF piezo-driven flexure-based mechanism. The experimental results show that the combined hysteresis model maintains the modeling accuracy over the entire work- ing range of the flexure-based mechanism. The mechanism＇s hysteresis is significantly suppressed by the use of the inverse PI model as the feedforward controller; and better result is achieved when a feedback loop is also incorporated. The tracking performance of the flexure-based mechanism is greatly improved.展开更多
软体机器人是近年来的研究热点,涉及信号检测、控制、致动等多个方面。基于弹性体材料设计制备了一种符合人体手指运动规律的弯曲致动器及其仿生手和电容式可拉伸传感器。仿生手总共设计有14个弯曲关节,且每个弯曲关节都由外部管道独立...软体机器人是近年来的研究热点,涉及信号检测、控制、致动等多个方面。基于弹性体材料设计制备了一种符合人体手指运动规律的弯曲致动器及其仿生手和电容式可拉伸传感器。仿生手总共设计有14个弯曲关节,且每个弯曲关节都由外部管道独立控制,当外部气压达到50 k Pa时,手指关节能够实现90o的弯曲变形,最大指尖压力为210m N。可拉伸应变传感器在200%应变下表现出良好的稳定性和可逆性,将传感器贴在手指关节上具有监测手指运动的功能。最后,将传感器贴于手套上作为可穿戴器件,通过单片机控制系统采集手套上传感器由于手指弯曲产生的信号,信号经蓝牙传输,实现对气动回路的有效控制,最终完成对仿生手的远程实时操控。研究结果可应用于远程交互、生命科学等领域。展开更多
基金Supported by National Natural Science Foundation of China (No. 51175372)National Key Special Project of Science and Technology of China (No. 2011ZX04016-011)
文摘Updating parameters according to the driving rate of input, the rate-dependent Prandtl-Ishlinskii （PI） model is widely used in hysteresis modeling and compensation. In order to improve the modeling accuracy, two PI models identified at low and high driving rates separately are incorporated through a combination law. For the piezo- driven flexure-based mechanism, the very low damping ratio makes it easy to excite the structural vibration. As a re- suit, the measured hysteresis loop is greatly distorted and the modeling accuracy of the identified P1 model is signifi- cantly affected. In this paper, a novel time-efficient parameter identification method which utilizes the superimposed sinusoidal signals as the control input is proposed. This method effectively avoids the excitation of the structural vibra- tion. In addition, as the driving rate of the superimposed sinusoidal signals covers a wide range, all the coefficients required for modeling the rate-dependence can be identified through only one set of experimental data. Hysteresis modeling and trajectory tracking experiments were performed on a 2-DOF piezo-driven flexure-based mechanism. The experimental results show that the combined hysteresis model maintains the modeling accuracy over the entire work- ing range of the flexure-based mechanism. The mechanism＇s hysteresis is significantly suppressed by the use of the inverse PI model as the feedforward controller; and better result is achieved when a feedback loop is also incorporated. The tracking performance of the flexure-based mechanism is greatly improved.
文摘软体机器人是近年来的研究热点,涉及信号检测、控制、致动等多个方面。基于弹性体材料设计制备了一种符合人体手指运动规律的弯曲致动器及其仿生手和电容式可拉伸传感器。仿生手总共设计有14个弯曲关节,且每个弯曲关节都由外部管道独立控制,当外部气压达到50 k Pa时,手指关节能够实现90o的弯曲变形,最大指尖压力为210m N。可拉伸应变传感器在200%应变下表现出良好的稳定性和可逆性,将传感器贴在手指关节上具有监测手指运动的功能。最后,将传感器贴于手套上作为可穿戴器件,通过单片机控制系统采集手套上传感器由于手指弯曲产生的信号,信号经蓝牙传输,实现对气动回路的有效控制,最终完成对仿生手的远程实时操控。研究结果可应用于远程交互、生命科学等领域。
