Lauren E. Linderman

Characterization of Wireless Smart Sensor Performance

A critical aspect of using wireless sensors for structural health monitoring is communication performance. Unlike wired systems, data transfer is less reliable between wireless sensor nodes owing to data loss. While reliable communication protocols are typically used to reduce data loss, this increase in communication can drain already limited power resources. This paper provides an experimental investigation of the wireless communication characteristics of the Imote2 smart sensor platform; the presentation is tailored toward the end user, e.g., application engineers and researchers. Following a qualitative discussion of wireless communication and packet delivery, a quantitative characterization of wireless communication capabilities of the Imote2 platform, including an assessment of onboard and external antenna performance, is provided. Herein, the external antenna was found to significantly outperform the onboard antenna in both transmission and reception reliability. However, the built environment, including building materials and other wireless networks, can significantly reduce reception rate and thus increase packet loss. Finally, implications of these results for a full-scale implementation are presented.

Low-Latency Data Acquisition Hardware for Real-Time Wireless Sensor Applications

Wireless sensor networks (WSNs) are an attractive alternative to traditional tethered systems for monitoring and feedback control of civil structures. In civil engineering, research has focused on the application of WSN to structural health monitoring (SHM); as a result, hardware has been tailored to SHM applications. However, the real-time performance requirements of WSNs for control are more stringent than for monitoring applications. Wireless communication, processing time, and data-acquisition hardware are a few of the many sources of time-delay in wireless control systems; this paper will focus on the latency due to the acquisition and actuation hardware in the control loop, i.e., the time between capturing a measurement and its availability on the processor. Previous work on smart sensor hardware focuses either on resolution for SHM applications or the actuation interface for control applications. Overall, an analysis of latency due to the data-acquisition hardware and an understanding of the inherent limitations have been lacking. This paper illustrates the limitations of a common analog-to-digital converter (ADC) architecture for SHM applications and presents a low-latency hardware solution for wireless control nodes. The performance of the two different data-acquisition techniques emphasizes the implication of ADC architecture on the latency and resolution of the data. Ultimately, through the use of an successive-approximation-register-type ADC and careful design of the corresponding driver, the latency due to the hardware is almost negligible.

Decentralized Active Control of Multistory Civil Structure with Wireless Smart Sensor Nodes

AbstractWireless smart sensors, a popular option for structural health monitoring, are an exciting alternative to traditional tethered systems for structural control. Their onboard communication, sensing, actuation, and processing capabilities offer all the components for feedback control to limit structural response during earthquakes and wind. However, wireless smart sensors pose unique challenges for structural control including communication latency, delays, and data loss. Previous research in wireless structural control has used decentralized control approaches to overcome these inherent limitations. However, these experimental investigations have focused on semiactive control systems, in which stability is guaranteed. Thus, the semiactive wireless control implementations are less sensitive to delays and sampling rate limitations imposed by the smart sensors. This paper presents an experimental investigation of decentralized wireless active control. All the elements of the wireless control system are...

Study of sparse feedback control algorithm on benchmark structure

Modern structural control systems use centralized, wired sensor feedback to impart counter forces based on measurement of the response. However, centralized systems can be sensitive to sensor failure, controller failure, and the reliability of sensor links. The recent study of wireless control systems has encouraged decentralized control approaches to overcome wireless structural control challenges, including limiting the wireless communication required and the associated slow sampling rate and time delays in the control system. Decentralized control offers the additional advantages of multiple independent controllers and small subsets of measurement feedback. Previous decentralized structural control algorithms enforce a spatial sparsity pattern during the design, which is assumed a priori. Therefore, the optimal feedback structure is not considered in the design. This work explores a decentralized optimal LQR design algorithm where the sparsity of the feedback gain is incorporated into the objective function. The control approach is compared to previous decentralized control techniques on a 5-Story control benchmark structure fitted with a semi-active control system. Additionally, the sparsity and control requirements are compared to centralized designs to gain insight on the overall performance of sparse feedback systems. The optimal sparse feedback design offers the best balance of performance, measurement feedback, and control effort.

Sparse feedback structures for wireless control of civil systems

Although originally popularized for structural health monitoring, wireless smart sensors are an attractive alternative to traditional tethered systems for structural control. Their onboard sensing, processing, and wireless communication offer all the components of a feedback control system. However, wireless smart sensors pose unique challenges for the application of centralized control, which is common in most modern control systems. Decentralized control offers several advantages to wireless structural control, including limiting the wireless communication required and the associated slow sampling rate and time delays in the control system. Previous decentralized structural control algorithms, both Ad-Hoc and Heuristic, enforce a spatial sparsity pattern during the design, which is assumed a priori. Therefore, the optimal feedback structure is not considered in the design. This work explores a decentralized optimal LQR design algorithm where the sparsity of the feedback gain is incorporated into the objective function. The control approach is compared to previous decentralized control techniques on the 20-Story control benchmark structure. Sparsity and control requirements are compared to centralized designs. The optimal sparse feedback design offers the best balance of performance, measurement feedback, and control effort. Additionally, the feedback structure identified is not easily identifiable a priori; thus, highlighting the significance of particular measurements in this feedback framework.

Impact of multiple access technique on wireless structural control

In a wireless structural control system, a linear quadratic Gaussian regulator, under measurements loss, is used to limit the RMS structural response to natural hazards. The analysis shows the dependence of the state estimation error, and corresponding control performance, to the packet error probability and transmission delay. These metrics are studied for two communication access techniques: code-division multiple-access (CDMA) and time-division multiple-access (TDMA). By simulating a 9-story benchmark building with real earthquake inputs, it is shown that CDMA scheme performs better with few sensors scenario due to the low latency, however, more sensors may decrease the closed-loop performance due to high delay (TDMA) or high lost rate (CDMA).