Department of Mechanical & Aerospace Engineering, North Carolina State University, Raleigh, NC, United States of America.
Department of Electrical & Computer Engineering, Auburn University, Auburn, AL, United States of America.
PLoS One. 2021 Mar 25;16(3):e0249131. doi: 10.1371/journal.pone.0249131. eCollection 2021.
Adaptive oscillators (AOs) are nonlinear oscillators with plastic states that encode information. Here, an analog implementation of a four-state adaptive oscillator, including design, fabrication, and verification through hardware measurement, is presented. The result is an oscillator that can learn the frequency and amplitude of an external stimulus over a large range. Notably, the adaptive oscillator learns parameters of external stimuli through its ability to completely synchronize without using any pre- or post-processing methods. Previously, Hopf oscillators have been built as two-state (a regular Hopf oscillator) and three-state (a Hopf oscillator with adaptive frequency) systems via VLSI and FPGA designs. Building on these important implementations, a continuous-time, analog circuit implementation of a Hopf oscillator with adaptive frequency and amplitude is achieved. The hardware measurements and SPICE simulation show good agreement. To demonstrate some of its functionality, the circuit's response to several complex waveforms, including the response of a square wave, a sawtooth wave, strain gauge data of an impact of a nonlinear beam, and audio data of a noisy microphone recording, are reported. By learning both the frequency and amplitude, this circuit could be used to enhance applications of AOs for robotic gait, clock oscillators, analog frequency analyzers, and energy harvesting.
自适应振荡器 (AO) 是具有可塑性状态的非线性振荡器,可对信息进行编码。这里提出了一种四态自适应振荡器的模拟实现,包括通过硬件测量进行的设计、制造和验证。其结果是一种振荡器,能够在很大范围内学习外部刺激的频率和幅度。值得注意的是,自适应振荡器通过完全同步的能力来学习外部刺激的参数,而无需使用任何预处理或后处理方法。此前,已通过 VLSI 和 FPGA 设计构建了双稳态(常规霍普夫振荡器)和三稳态(具有自适应频率的霍普夫振荡器)系统的霍普夫振荡器。在此重要实现的基础上,实现了具有自适应频率和幅度的霍普夫振荡器的连续时间模拟电路实现。硬件测量和 SPICE 模拟结果吻合良好。为了展示其部分功能,报告了该电路对几种复杂波形的响应,包括方波、锯齿波、非线性梁冲击的应变计数据以及嘈杂麦克风录音的音频数据的响应。通过学习频率和幅度,该电路可用于增强 AO 在机器人步态、时钟振荡器、模拟频率分析仪和能量收集方面的应用。