Active structures integrated with wireless sensor and actuator networks: a bio-inspired control framework

One of the main problems in controlling the shape of active structures (AS) is to determine the actuations that drive the structure from the current state to the target state. Model-based methods such as stochastic search require a known type of load and relatively long computational time, which limits the practical use of AS in civil engineering. Moreover, additive errors may be produced because of the discrepancy between analytic models and real structures. To overcome these limitations, this paper presents a compound system called WAS, which combines AS with a wireless sensor and actuator network (WSAN). A bio-inspired control framework imitating the activity of the nervous systems of animals is proposed for WAS. A typical example is tested for verification. In the example, a triangular tensegrity prism that aims to maintain its original height is integrated with a WSAN that consists of a central controller, three actuators, and three sensors. The result demonstrates the feasibility of the proposed concept and control framework in cases of unknown loads that include different types, distributions, magnitudes, and directions. The proposed control framework can also act as a supplementary means to improve the efficiency and accuracy of control frameworks based on a common stochastic search.中文概要目的主动结构的几何形状控制一直是结构控制领域的研究前沿。为满足形状控制目标,一个主要问题就是如何求解主动构件的执行量。以随机搜索算法为核心的基于模型的控制方法逐渐成为主流,但仍存在若干需改善之处,如计算量大、实时性差、不能很好应对未知荷载作用以及实际结构与数值模型间存在差异等。本研究旨在寻求一种控制框架,使其能够在某些场合下具备更好的控制性能。创新点1. 提出一类主动结构混合系统——集成无线传感器-执行器网络的主动结构(WAS);2. 通过模仿动物反射活动以及节律运动,提出WAS 的两层级仿生控制框架。方法1. 将无线传感器-执行器网络引入主动结构,组成混合系统,建立离散的基本模型(图1));2. 结合仿生思想,按照两层级控制框架编制基本控制流程(图3 和4);3. 通过仿真模拟,将无线传感器-执行器网络嵌入主动三棱柱张拉整体结构,运用仿生控制框架对张拉整体结构在多种工况下进行形状控制,验证所提概念和方法的可行性与有效性(图7 和13)。结论1. 与以随机搜索算法为主的基于模型的形状控制方法相比,本文所提出的混合系统及其仿生控制框架,计算量极小,因此可快速应对外部作用的变化; 2. 对于未知荷载作用,本文提出的仿生控制框架无需进行荷载识别,因此适应性更强; 3. 由于不依赖于有限元模型,该仿生控制框架避免了来自实际结构与数值模型的误差,因此控制精度更高。