A feedback control principle common to several biological and engineered systems.
A feedback control principle common to several biological and engineered systems.
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DOI：
10.1098/rsif.2021.0711
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Navlakha S
中科院分区：
文献类型：
--
作者：
Suen JY;Navlakha S
反馈控制被许多分布式系统用来优化行为。传统的反馈控制算法花费大量的资源来不断地感知和稳定感兴趣的连续控制变量，例如用于实现巡航控制的车辆速度，或者用于维持动态平衡的体温。相比之下，离散事件反馈(例如，服务器确认数据何时成功传输，或在蚂蚁成功觅食后返回巢穴时进行短暂的天线交互)可以降低与监测连续变量相关的成本；然而，在这种设置下优化行为需要替代策略。在这里，我们研究了生物系统和工程系统中离散事件反馈控制策略的相似之处。我们发现，不同的生物系统都使用两种常见的工程规则--正反馈时的加法增加和负反馈时的乘性减少，以及乘性增加乘性减少，包括调节收割机蚁群的觅食，维持细胞大小的动态平衡，以及神经回路中的突触学习和适应。这些规则支持这些系统的几个目标，包括优化效率(即使用所有可用资源)；在合作的代理之间公平地分配资源，或者相反地，在竞争的代理之间快速获取资源；以及最小化响应的延迟，特别是当条件改变时。我们假设，分布式计算的理论框架可能为分析生物系统的适应行为提供新的方法，反过来，生物策略可能会启发工程上离散事件反馈控制的新算法。
Feedback control is used by many distributed systems to optimize behaviour. Traditional feedback control algorithms spend significant resources to constantly sense and stabilize a continuous control variable of interest, such as vehicle speed for implementing cruise control, or body temperature for maintaining homeostasis. By contrast, discrete-event feedback (e.g. a server acknowledging when data are successfully transmitted, or a brief antennal interaction when an ant returns to the nest after successful foraging) can reduce costs associated with monitoring a continuous variable; however, optimizing behaviour in this setting requires alternative strategies. Here, we studied parallels between discrete-event feedback control strategies in biological and engineered systems. We found that two common engineering rules—additive-increase, upon positive feedback, and multiplicative-decrease, upon negative feedback, and multiplicative-increase multiplicative-decrease—are used by diverse biological systems, including for regulating foraging by harvester ant colonies, for maintaining cell-size homeostasis, and for synaptic learning and adaptation in neural circuits. These rules support several goals of these systems, including optimizing efficiency (i.e. using all available resources); splitting resources fairly among cooperating agents, or conversely, acquiring resources quickly among competing agents; and minimizing the latency of responses, especially when conditions change. We hypothesize that theoretical frameworks from distributed computing may offer new ways to analyse adaptation behaviour of biology systems, and in return, biological strategies may inspire new algorithms for discrete-event feedback control in engineering.
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