具有展向柔性的柔性箔的神经控制

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS
Annika-verena Haecker, Gabriel N. Carryon, J. Tangorra, T. Sattel
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引用次数: 0

摘要

改变柔顺型翼片弯曲刚度的空间分布可以提高翼片的游泳性能,但对基于神经控制的翼片提出了挑战。同样的特性,使得这些箔的有效推进也使它们具有挑战性的控制与一个神经振荡器,即在机械性能的变化将导致振幅和相位的感官反馈信号的变化取决于传感器的位置。在这项研究中,我们研究了传感器放置对一个耦合系统的夹带特性的影响,该系统由一个神经振荡器组成,驱动一系列具有展向柔性的柔性箔(即在背-腹方向上空间变化的机械性能)。我们发现,从箔的刚性区域获取感官反馈产生了一个更广泛的频率范围,在其中夹带发生相比,从箔的柔性区域获取反馈。此外,我们表征推力和升力产生的跨向箔作为箔的扑动频率和弯曲刚度的函数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural-Based Control of Compliant Foils With Spanwise Flexibility
The ability to change the spatial distribution of a compliant foil’s flexural rigidity can enhance the foil’s swimming performance capabilities but pose challenges to neural-based control of these types of foils. The same property that makes these foil’s effective propulsors also makes them challenging to control with a neural oscillator, namely the variation in the mechanical properties will cause the amplitude and phase of the sensory feedback signal to vary depending upon the placement of the sensor. In this study we investigate the effect of sensor placement on the entrainment characteristics of a coupled-system consisting of a neural oscillator driving a series of compliant foils with spanwise flexibility (i.e. spatially varying mechanical properties in the dorsal-ventral direction). We find that acquiring sensory feedback from the foil’s stiff region produces a broader range of frequencies over which entrainment occurs compared to acquiring feedback from the compliant region of a foil. Additionally, we characterize the thrust and lift forces generated by spanwise foils as a function of the foil’s flapping frequency and flexural rigidity.
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来源期刊
Mechatronic Systems and Control
Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.40
自引率
66.70%
发文量
27
期刊介绍: This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.
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