Development of hardware neural networks generating driving waveform for electrostatic actuator
Development of hardware neural networks generating driving waveform for electrostatic actuator
复制标题
静电致动器驱动波形硬件神经网络的开发
DOI：
10.1007/s10015-020-00608-4
复制
发表时间：
2020
影响因子：
0.9
通讯作者：
Saito Ken
中科院分区：
文献类型：
--
作者：
Sasaki Takuro;Kurosawa Mika;Ohara Masaya;Hayakawa Yuichiro;Noguchi Daisuke;Takei Yuki;Kaneko Minami;Uchikoba Fumio;Saito Ken
作者正在研究用硬件神经网络（HNN）来控制微型机器人系统的运动。在之前的研究中，波形发生器被用来驱动微型机器人的静电致动器。一旦用HNN构造驱动电路，控制电路和驱动电路就可以集成到一个芯片上。在本文中，作者将提出一种基于HNN的驱动电路。HNN由2个自振荡细胞体模型、6个分离兴奋细胞体模型、4个兴奋突触模型和6个抑制突触模型组成。单个自振荡胞体模型输出频率为3mhz的电振荡方波。提出的HNN在不使用大电容的情况下产生较长的延迟。结果表明，所提出的HNN可以产生可变频率的静电致动器驱动波形。驱动波形的频率可以在50到100赫兹之间变化。此外，所提出的HNN与中央模式发生器（CPG）模型相连接。利用该模型输出静电致动器的驱动波形，实现微型机器人的三脚架步态模式。
The authors are studying to control the locomotion of the microrobot system using hardware neural networks (HNN). In previous research, a waveform generator was used to drive the electrostatic actuators of the microrobot. Once the driving circuit is constructed using HNN, the controlling circuit and the driving circuit can be integrated into a single chip. In this paper, the authors will propose the driving circuit using HNN. The HNN consists of two self-oscillating cell body models, six separately-excited cell body models, four excitatory-synaptic models, and six inhibitory-synaptic models. The single self-oscillating cell body model outputs the electrical oscillated square waveform as 3 MHz of frequency. The proposed HNN generates a long delay without using large capacitors. As a result, the proposed HNN can generate the driving waveform of electrostatic actuators with variable frequency. The frequency of the driving waveform could vary from 50 to 100 Hz. Also, the proposed HNN connected to the Central Pattern Generator (CPG) model. The CPG model with proposed HNN outputs the driving waveform of the electrostatic actuator which can perform the tripod gait pattern of the microrobot.
DOI：
10.23919/icep.2018.8374664
发表时间：
2018
期刊：
2018 International Conference on Electronics Packaging and iMAPS All Asia Conference (ICEP-IAAC)
影响因子：
--
作者：
Ken Saito;D. Contreras;Y. Takeshiro;Y. Okamoto;Y. Nakata;Taisuke Tanaka;S. Kawamura;M. Kaneko;F. Uchikoba;Y. Mita;K. Pister
通讯作者：
K. Pister
昆虫型微型机器人可切换步态模式的硬件神经网络IC的开发
DOI：
--
发表时间：
2019
期刊：
Proceedings of the 2019 IEEE/SICE International Symposium on System Integration Paris, France, January 14-16, 2019
影响因子：
--
作者：
Masaya Ohara;Mika Kurosawa;Takuro Sasaki;Taisuke Tanaka;Satoshi Kawamura;Yuichiro Hayakawa;Daisuke Noguchi;Minami Kaneko;Fumio Uchikoba and Ken Saito
通讯作者：
Fumio Uchikoba and Ken Saito
脉冲型硬件混沌神经元模型及其分岔现象
DOI：
10.1016/s0893-6080(98)00099-9
发表时间：
1999
期刊：
Neural networks : the official journal of the International Neural Network Society
影响因子：
--
作者：
K. Someya;Hidekazu Shinozaki;Y. Sekine
通讯作者：
Y. Sekine