基于记忆的事件触发反馈环的类极限环渐近动力学

U. Premaratne
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引用次数: 2

摘要

基于记忆的事件触发(MBET)是一种降低控制系统有效采样率的实用方法。在MBET中,对周期性采样的状态变量和控制输入进行编码,使得只有当它们的变化值超过预定的阈值时才进行数据传输。这导致网络控制系统(NCS)的网络流量显著减少。如果MBET编码导致渐近循环行为(类似极限环的行为),则会导致所需带宽的下界。本文研究了这种循环形成的必要条件。通过解析和经验模拟证明,渐近动态循环发生的主要条件是标量控制输入与系统将收敛到恒定控制输入的唯一平衡点之间的微分同构。
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Limit cycle like asymptotic dynamics in feedback loops with Memory Based Event Triggering
Memory Based Event Triggering (MBET) is a pragmatic approach to reduce the effective sampling rate of a control system. In MBET, periodically sampled state variables and control inputs are encoded such that data transmission takes place only when they change by a value that exceeds a predetermined threshold. This results in a significant reduction of network traffic in Networked Control Systems (NCS). Should MBET encoding result in an asymptotic cyclic behavior (limit cycle like behavior), it would result in a lower bound in the required bandwidth. In this paper, the necessary conditions for the formation of such a cycle are investigated. It is demonstrated analytically as well as through empirical simulations that the main requirement for the occurrence of asymptotic dynamic cycles is a diffeomorphism between a scalar control input and a unique equilibrium point to which the system will converge to for a constant control input.
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