Development of neural networks chip generating driving waveform for electrostatic motor

Author:

Sasaki Takuro,Kurosawa Mika,Usami Yu,Kato Shinya,Sakaki Arisa,Takei Yuki,Kaneko Minami,Uchikoba Fumio,Saito Ken

Abstract

AbstractThe authors are studying hardware neural networks (HNN) to control the locomotion of the microrobot. The neural networks chip is the integrated circuit chip of the HNN. We proposed the electrostatic motor that is the new actuator of the microrobot in our previous research. The electrostatic motor used the waveform generator to generate the driving waveform. In this paper, the authors will propose the driving circuit using neural networks chip. The cell body model is the basic component of the neural networks chip that outputs 3 MHz frequency of electrical oscillated pulse waveform. Therefore, large capacitors need to connect outside of the neural networks chip to generate the low-frequency driving waveform. The proposal neural networks chip generates a long delay without using large capacitors. In addition, the neural networks chip generated a two-phase anti-phase synchronized waveform by incorporating a mechanism for adjusting synaptic weight. As a result, the proposal neural networks chip can generate the electrostatic motor’s driving waveform with variable frequency. The frequency of the driving waveform could vary from 40 to 126 Hz.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,General Biochemistry, Genetics and Molecular Biology

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Position Self-Sensing for Electrostatic Motors Under Amplitude-Modulated AC Operation;IEEE Transactions on Industrial Electronics;2023

2. Development of neural networks integrated circuit driving electrostatic motors for microrobot;Artificial Life and Robotics;2022-11-27

3. Research status and development trends of Electrostatic Motor;Highlights in Science, Engineering and Technology;2022-11-10

4. Development of a Receptor Cell Model for Artificial Life;Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning;2022

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