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Wireless Sensor Networks in Smart Structural
Technologies
Yang Wang 1 , Kincho H. Law 2
1 Georgia Institute of Technology, Atlanta, Georgia, USA
2 Stanford University, Stanford, California, USA
1. Introduction
Recent advances in wireless communication, as well as embedded computing, have opened many new exciting opportunities for wireless sensor networks. Miniature and low-cost wireless sensors are expected to become available in the next decade, offering countless possibilities for a wide range of applications. Among them is smart structural technology, an active research domain that holds significant promise for enhancing infrastructure management and safety. A smart structure refers to a specially equipped structure (e.g. buildings, bridges, dams, etc.) that can monitor and react to surrounding environment and the structure’s own conditions, in a pre-designed and beneficial manner.
Smart structural technology encompasses at least two major fields, i.e. structural health monitoring and structural control. A structural health monitoring (SHM) system measures structural responses and predicts, identifies, and locates the onset of structural damage, e.g. due to deterioration or hazardous events. Structural sensors, such as micro-electro- mechanical system (MEMS) accelerometers, metal foil strain gages, fiber optic strain sensors, among others, have been developed and employed to collect important information about civil structures that could be used to infer the safety conditions of the structure (Farrar, et al. 2003, Sohn, et al. 2003, Chang 2009). On the other hand, structural control technology aims to mitigate adverse effects due to excessive dynamic loads (Yao 1972, Soong 1990, Housner, et al. 1997, Spencer and Nagarajaiah 2003).
Structural monitoring and control both involve acquiring response data in real time. In order to transmit real-time data, coaxial cables are normally employed as the primary communication link. Cable installation is labor intensive and time consuming, and can cost as much as $5,000 US dollars per communication channel (Çelebi 2002). To eradicate the high cost incurred by the use of cables, wireless systems could serve as a viable alternative
(Straser and Kiremidjian 1998). Wireless communication standards, such as Bluetooth (IEEE 802.15.1), Zigbee (IEEE 802.15.4), Wi-Fi (IEEE 802.11b), are now mature and reliable technologies widely adopted in many industrial applications (Cooklev 2004). Potential applications of wireless technologies in structural health monitoring have been explored by a number of researchers, as reviewed by Lynch and Loh (2006). By incorporating a control interface, wireless sensors have also been extended to potentially command control devices for structural control applications (Wang, et al. 2007b).
Compared to cable-based systems, wireless structural monitoring and control systems have a unique set of advantages and technical challenges. Besides the desire for portable long- lasting energy sources, such as batteries, reliable data communication is a key issue for implementation. The purpose of this chapter is to review the important issues and metrics for adopting wireless sensor networks in smart structural systems. In a structural health monitoring system, sensors are typically deployed in a passive manner, primarily for measuring structural responses. Structural control systems, on the other hand, need to respond in real time to mitigate excess dynamic response of structures. Typical feedback control systems require real-time information and measurements to instantly determine control decisions. Although structural monitoring and control applications pose different needs and requirements, efficient information flow plays a key and critical role in both implementations. For example, the transmission latency and limited bandwidth of wireless devices can impede real-time operations as required by control or monitoring systems. In addition, communication in a wireless network is inherently less reliable than that in cable-based systems, particularly when node-to-node communication range lengthens. These information constraints, including bandwidth, latency, range, and reliability, need to be considered carefully using an integrated system approach and pose many challenges in the selection of hardware technologies and the design of software/algorithmic strategies.
The chapter adopts a previously designed wireless structural monitoring and control system as an example to discuss various intriguing research challenges (Wang, et al. 2005, Wang 2007). The system contains wireless sensing and control units that can be used for both wireless structural health monitoring and real-time feedback structural control. Modularized software is designed for the wireless units, so that application programs can be conveniently embedded into the units. The architectural details of the wireless structural monitoring and control system are presented. For different structural applications, including health monitoring and control, special communication protocols have been designed to efficiently manage the information flow among the wireless units. Laboratory and field validation tests have been conducted to assess the performance of the prototype wireless structural monitoring and control system.
2. Design and implementation of a wireless sensing and control unit
Sensing and control units are the fundamental components of a wireless monitoring and control system. The prototype wireless unit is designed in such a way that the unit can serve as either a sensing unit (i.e. a unit that collects data from sensors and wirelessly transmits the data), a control unit (i.e. a unit that calculates optimal control decisions and
Design Parameter Specification Computing Core Microcontroller 8-bit RISC^1 architecture, up to 16MIPS^2 throughput at 16MHz Flash Memory 128K bytes Internal SRAM^3 4K bytes External SRAM 128K bytes EEPROM^4 4K bytes Power Consumption 30mA active, 55μA standby Wireless Transmission 9XCite 24XStream Operating Frequency ISM 902-928 MHz ISM 2.4000 - 2.4835 GHz Data Transfer Rate 38.4 kbps 19.2 kbps Communication Range Up to 300' (90m) indoor, 1000' (300m) at line-of-sight
Up to 600' (180m) indoor, 3 miles (5km) at line-of-sight Power Consumption 55mA transmitting, 35mA receiving, 20μA standby
150mA transmitting, 80mA receiving, 26μA standby Sensing Interface 4 channels, 16-bit, up to 100 kHz Control Interface 1 channel, 16-bit, up to 1 MHz Physical Size 10.2cm × 6.5cm × 4.0cm
Table 1. Key parameters of the wireless sensing unit.
In order to manage the hardware components in a wireless sensing unit, software modules are implemented and embedded in the ATmega128 microcontroller. For the ATmega microcontroller, software can be written in a high-level programming language, such as C, compiled into binary instructions, and loaded into the non-volatile flash memory of the microcontroller. When the wireless unit is powered on for normal operation, the microcontroller automatically starts executing the embedded instructions. The software design of the wireless sensing and control units follows the hierarchical structure as shown in Fig. 2. At the bottom level are the software modules that manage the basic peripherals of the microcontroller. The middle layer consists of software modules that manage other onboard hardware components. Specific software modules for structural health monitoring and control are implemented in the top level application layer.
As shown in Fig. 2, the lowest level of the embedded software manages the peripherals of the ATmega128 microcontroller and serves as the fundamental modules to support the functions of other hardware components. Embedded modules include: timer interrupt functions, byte-by-byte communication through the UART and SPI ports, and internal memory management. The timer interrupt service is implemented to achieve a constant time step for sensor data sampling. The interrupt function is also a powerful feature that allows the software to momentarily pause an executing task (such as data processing or wireless communication) to sample data from the sensing interface according to a precise timing schedule. Immediately after servicing the sensing interface, the paused task is resumed and the program continues its execution. This timer interrupt feature is utilized
(^1) RISC: reduced instruction set computer. (^2) MIPS: million instructions per second. (^3) SRAM: static random access memory. (^4) EEPROM: electrically erasable programmable read-only memory.
to implement continuous data streaming from multiple wireless sensing units, where sensor data sampling has to occur at a constant sampling step amidst the execution of the wireless communication or data interrogation program. In effect, the software supports concurrency thereby allowing multiple software tasks to execute at the same time.
Building on top of the microcontroller peripherals are the software drivers that manage other hardware components in the wireless unit. Utilizing the UART peripheral, the wireless communication driver provides the following functions interfacing the microcontroller with the wireless transceiver: 1) reading or setting the radio parameters of the attached wireless transceiver; 2) sending or receiving data through the wireless transceiver; 3) implementing the state machine representing the wireless communication protocol. A driver module is implemented to manage the 128kB external Static Random Access Memory (SRAM). This module includes functions to enable and disable the external SRAM, as well as functions that allow access to the lower 64kB half or higher 64kB half of the memory chip. The other two hardware drivers, the A2D and the D2A modules, manage the interfaces with the structural sensors and control devices. The ATmega microcontroller provides only one SPI port, which is shared by both the A2D converter (ADS8341) for sensing and the D2A converter (AD5542) for control. The A2D module commands the ADS8341 to convert a 0 to 5V analog sensor signal into a 16-bit integer. Knowing the sensitivity and offset of the sensor signal, the microcontroller can then compute a floating-point number quantifying the physical parameter being measured by the sensor. Conversely, the D2A module takes a floating-point number between -5V and 5V as input, converts the number into a 16-bit integer, and pushes the integer to the AD5542 to output the corresponding control voltage signal.
Utilizing the hardware drivers for communication, computing, sensing, and control, software can be developed to support structural health monitoring and control applications. A number of engineering algorithms, such as Fast Fourier Transform (FFT), autoregressive (AR) analysis, linear quadratic regulator (LQR) control, and Kalman Filter, have been implemented and embedded in the wireless units. The ability to execute embedded
Fig. 2. Three-layer software architecture for the ATmega128 microcontroller in the wireless sensing and control unit.
In the prototype wireless sensing and control system, the setup parameters of the 24XStream transceiver are first tuned to minimize the transmission latency, TLatency. Then experiments are conducted to measure the actual achieved TLatency, which turns out to be around 15 ±0.5ms. The UART data rate of the 24XStream radio, RUART, is selected as 38400 bps in the implementation. For example, if a data packet sent from a sensing unit to a control unit contains 11 bytes, the total time delay for a single transmission is estimated to be:
×
TSingleTransm = + ≈ (ms) (2)
This amount of latency typically has minimal effect in most monitoring applications, but has noticeable effects to the timing-critical feedback control applications. This single- transmission delay represents one communication constraint that needs to be considered when calculating the upper bound for the maximum sampling rate of the control system. A few milliseconds of safety cushion time at each sampling step are a prudent addition that allows a certain amount of randomness in the wireless transmission latency without undermining the reliability of the communication system. Although the achievable transmission latency, TLatency, is around 15ms for the MaxStream 24XStream transceiver, it can be as low as 5ms for the 9XCite transceiver. This lower latency makes the 9XCite transceiver more suitable for real-time feedback control applications compared with the 24XStream transceiver. However, the 9XCite transceiver may only be used in countries and regions where the 900MHz band is for free public usage, such as the North America, Israel, South Korea, among others. On the other hand, operating in the 2.4GHz international ISM (Industrial, Science, and Medical) band, the 24XStream transceiver can be used in most countries in the world.
The other two constraints, reliability and range, are related to the attenuation of the wireless signal traveling along the transmission path. The path loss PL (in decibel) of a wireless signal is measured as the ratio between the transmitted power, PTX [mW], and the received
power, PRX [mW] (Molisch 2005):
[ ] (^10) dB 10log [mW] [mW]
= TX
RX
PL P
P
Path loss generally increases with the distance, d, between the transmitter and the receiver. However, the loss of signal strength varies with the environment along the transmission path and is difficult to quantify precisely. Experiments have shown that a simple empirical model may serve as a good estimate to the mean path loss (Rappaport and Sandhu 1994):
[ ] (^0 )^10 [ ] 0
( ) dB = [dB] + 10 log ^ + σ dB
PL d PL d n d X d (4)
Here PL d( 0 ) is the free-space path loss at a reference point close to the signal source (d 0 is
usually selected as approximately 1 meter). X σ represents the variance of the path loss,
which is a zero-mean log-normally-distributed random variable with a standard deviation
of σ. The parameter n is the path loss exponent that describes how fast the wireless signal
attenuates over distance. Basically, Eq. (4) indicates an exponential decay of signal power:
[ ] 0 [ ] 0
mW mW
− = ^
n P RX P dd (5)
where P 0 is the received power at the reference distance d 0. Typical values of n are
reported to be between 2 and 6. Table 2 shows examples of measured n and σ values in
different buildings for 914 MHz signals (Rappaport and Sandhu 1994).
Building n σ [dB]
Grocery store 1.8 5. Retail store 2.2 8. Suburban office building – open plan 2.4 9. Suburban office building – soft partitioned 2.8 14. Table 2. Values of path loss exponent n at 914MHz.
A link budget analysis can be used to estimate the range of wireless communication (Molisch 2005). To achieve a reliable communication link, it is required that
PTX [dBm]+ AG[dBi] ≥ PL d ( )[dB] + RS[dBm] +FM[dB] (^) (6)
where AG denotes the total antenna gain for the transmitter and the receiver, RS the receiver sensitivity, FM the fading margin to ensure quality of service, and PL d( ) the realized path loss at some distance d within an operating environment. Table 3 summarizes the link budget analysis for the 9XCite and 24XStream transceivers, and their estimated indoor ranges.
9XCite 24XStream P TX [dBm]^ 0.00 16. AG [dBi] (^) 4.00 4. RS [dBm] (^) -104.00 -105. FM [dB] (^) 22.00 22.
PL = PTX +AG-RS-FM [dB] 86.00 103.
PL d ( 0 ) [dB], d 0 = 1 m (^) 31.53 40.
PL − PL d ( 0 ) [dB]^ 54.47 63. n (^) 2.80 2. d [m]^ 88.20^ 192. Table 3. Link budget analysis to the wireless transceivers.
The path loss exponent n is selected to be 2.8, which is the same as the soft-partitioned office building in Table 2. Generally, 2.4GHz signals typically have higher attenuation than
data or analysis results can be viewed remotely from other computers over the Internet. Since the server and the wireless sensing units must communicate frequently with each other, portions of their software are designed in tandem to allow seamless integration and coordination.
Fig. 4. An overview of the prototype wireless structural sensing system.
At the beginning of each wireless structural sensing operation, the server issues commands to all the units, informing the units to restart and synchronize. After the server confirms that all the wireless sensing units have restarted successfully, the server queries the units one by one for the data they have thus far collected. Before the wireless sensing unit is queried for its data, the data is temporarily stored in the unit’s onboard SRAM memory buffer.
A unique feature of the embedded wireless sensing unit software is that it can continue collecting data from interfaced sensors in real-time as the wireless sensing unit is transmitting data to the server. In its current implementation, at each instant in time, the server can only communicate with one wireless sensing unit. In order to achieve real-time continuous data collection from multiple wireless sensing units with each unit having up to four analog sensors attached, a dual stack approach has been implemented to manage the SRAM memory (Wang, et al. 2007a). When a wireless sensing unit starts collecting data, the embedded software establishes two memory stacks dedicated to each sensing channel for storing the sensor data. For each sensing channel, at any point in time, only one of the stacks is used to store the incoming data stream. While incoming data is being stored into the dedicated memory stack, the system transfers the data in the other stack out to the server. For each sensing channel, the role of the two memory stacks alternate as soon as one stack is filled with newly collected data.
3.2 Communication design of the wireless structural health monitoring system
To ensure reliable wireless communication between the server and the wireless units, the communication protocol needs to be carefully designed and implemented. The commonly used network communication protocol is the Transmission Control Protocol (TCP) standard.
TCP is a sliding window protocol that handles both timeouts and retransmissions. It establishes a full duplex virtual connection between two endpoints. Although TCP is a reliable communication protocol, it is too general and cumbersome to be employed by the low-power and low data-rate communication such as in a wireless structural sensing network. The relatively long latency of transmitting each wireless packet is another bottleneck that may slow down the communication throughput. For practical and efficient application in a wireless structural sensing network, a simpler communication protocol is needed to minimize transmission overhead. Yet the protocol has to be designed to ensure reliable wireless transmission by properly addressing possible data loss. The communication protocol designed for the prototype wireless sensing system inherits some useful features of TCP, such as data packetizing, sequence numbering, timeout checking, and retransmission. Based upon pre-assigned arrangement between the server and the wireless units, the sensor data stream is segmented into a number of packets, each containing a few hundred bytes. A sequence number is assigned to each packet so that the server can request the data sequentially.
To simplify the communication protocol, special characteristics of the structural health monitoring application are exploited. For example, since the objective in structural monitoring application is normally to transmit sensor data or analysis results to the server, the server is assigned the responsibility for ensuring reliable wireless communication. As the server program normally runs on a computer and the wireless unit program runs on a microcontroller, it is also reasonable to assign the responsibility to the server since it has much higher computing power. For example, communication is always initiated by the server. After the server sends a command to the wireless sensing unit, if the server does not receive an expected response from the unit within a certain time limit, the server will resend the last command again until the expected response is received. However, after a wireless sensing unit sends a message to the server, the unit does not check if the message has arrived at the server correctly or not, because the communication reliability is assigned to the server. The wireless sensing unit only becomes aware of the lost data when the server queries the unit for the same data again. In other words, the server plays an “active” role in the communication protocol while the wireless sensing unit plays more of a “passive” role.
Finite state machine concepts are employed in designing the communication protocol for the wireless sensing units and the server. A finite state machine consists of a set of states and definable transitions between the states (Tweed 1994). At any point in time, the state machine can only be in one of the possible states. In response to different events, the state machine transits between its discrete states. The communication protocol for initialization and synchronization can be found in (Wang, et al. 2007a). Fig. 5(a) shows the communication state diagram of the server for one round of sensor data collection, and Fig. 5(b) shows the corresponding state diagram of the wireless units. During each round of data collection, the server collects sensor data from all of the wireless units; note that the server and the units have separate sets of state definitions.
m^ 6.
m^ 6.
(a) Plan view of the bridge illustrating locations of wired and wireless sensing systems.
(b) Elevation view to section A-A. (c) Side view of the bridge over Interstate 5. Fig. 6. Voigt Bridge test comparing the wireless and wired sensing systems.
Girder cells along the north side of the bridge are accessible through four manholes on the bridge sidewalk. As a testbed project for structural health monitoring research, a cable- based system has been installed in the northern-most cells of the box girder. The cable- based system includes accelerometers, strain gages, thermocouples, and humidity sensors. For the purpose of validating the proposed wireless structural monitoring system, thirteen accelerometers interfaced to wireless sensing units are installed within the two middle spans of the bridge to measure vertical vibrations. One wireless sensing unit (associated with one signal conditioning module and one accelerometer) is placed immediately below the accelerometer associated with the permanent wired monitoring system. While the wired accelerometers are mounted to the cell walls, wireless accelerometers are simply mounted on the floor of the girder cells to expedite the installation process. The installation and calibration of the wireless monitoring system, including the placement of the 13 wireless sensors, takes about an hour. The Maxstream 9XCite wireless transceiver operating at 900MHz is integrated with each wireless sensing unit.
Two types of accelerometers are associated with each monitoring system. At locations #3, 4, 5, 9, 10, and 11 in Fig. 6(a), PCB Piezotronics 3801 accelerometers are used with both the cabled and the wireless systems. At the other seven locations, Crossbow CXL01LF accelerometers are used with the cabled system, while Crossbow CXL02LF1Z accelerometers are used with the wireless system. Table 4 summarizes the key parameters of the three types of accelerometers. Signal conditioning modules are used for filtering noise, amplifying and shifting signals for the wireless accelerometers. The signals of the wired accelerometers are directly digitized by a National Instruments PXI-6031E data acquisition board (Fraser, et al. 2006). Sampling frequencies for the cable-based system and the wireless system are 1,000 Hz and 200 Hz, respectively.
Specification PCB3801 CXL01LF1 CXL02LF1Z Sensor Type Capacitive Capacitive Capacitive Maximum Range (^) ± 3g ± 1g ± 2g Sensitivity 0.7 V/g 2 V/g 1 V/g Bandwidth 80 Hz 50Hz 50Hz RMS Resolution (Noise Floor) 0.5 mg 0.5 mg 1 mg Minimal Excitation Voltage 5 ~ 30 VDC 5 VDC 5 VDC
Table 4. Parameters of the accelerometers used by the wire-based and wireless systems in the Voigt Bridge test.
The bridge is under normal traffic operation during the tests. Fig. 7(a) shows the time history data at locations #6 and #12, collected by the cable-based and wireless monitoring systems when a vehicle passes over the bridge. A close match is observed between the data collected by the two systems. The minor difference between the two data sets can be mainly attributed to two sources: 1) the signal conditioning modules are used in the wireless system but not in the cabled system; 2) the wired and wireless accelerometer locations are not exactly adjacent to each other, as previously described. Fig. 7(b) shows the Fourier spectra determined from the time history data. The FFT results using the data collected by the cabled system are computed offline, while the FFT results corresponding to the wireless data are computed online in real-time by each wireless sensing unit. After each wireless
-5 0 2 4 6 8
0
5 x 10
-
Acceleration (g)^ Wired #
-5 0 2 4 6 8
0
5 x 10
-
Time (s)
Wired #
-5 0 2 4 6 8
0
5 x 10
-
Acceleration (g)^ Wireless #
-5 0 2 4 6 8
0
5 x 10
-
Time (s)
Wireless #
(a) Comparison between wired and wireless time history data
(^00 5 10 )
2
4
FFT Magnitude
Wired #
(^00 5 10 )
2
4
Frequency (Hz)
Wired #
(^00 5 10 )
0.
FFT Magnitude
Wireless #
(^00 5 10 )
0.
Frequency (Hz)
Wireless #
(b) Comparison between FFT to the wired data, as computed offline by a computer, and FFT to the wireless data, as computed online by the wireless sensing units Fig. 7. Comparison between wired and wireless data for the Voigt Bridge test.
Fig. 9 shows the operating deflection shapes (ODS) extracted from one set of test data collected during a hammer excitation test. The hammer excitation is applied at the location shown in Fig. 8(a) and during intervals of no passing vehicles. DIAMOND, a modal analysis software package, is used to extract the operating deflection shapes (ODS) of the bridge deck (Doebling, et al. 1997). Under hammer excitation, the operating deflection shapes at or near a resonant frequency should be dominated by a single mode shape (Richardson 1997). Fig. 9 presents the first four dominant operating deflection shapes of the bridge deck using wireless acceleration data. The ODS #1 (4.89 Hz), #2 (6.23 Hz), and # (11.64 Hz) show primarily flexural bending modes of the bridge deck; a torsional mode is observed in ODS #3 (8.01 Hz). Successful extraction of the ODS shows that the acceleration data from the 20 wireless units are well synchronized.
-60 -40 (^) -20 0 20 40 60 -
0 -0.
0
ODS #1, 4.89Hz
-60 -40 (^) -20 0 20 40 60 -
0 -0.
0
ODS #2, 6.23Hz
-60 -40 -20 0 20 40 60 -
0 -0.
0
ODS #3, 8.01Hz
-60 -40 -20 0 20 40 60 -
0 -0.
0
ODS #4, 11.64Hz
Fig. 9. Operating deflection shapes extracted from wireless sensor data.
4. Wireless structural control
A feedback structural control system contains an integrated network of sensors, controller, and control devices. When external excitation (such as an earthquake or typhoon) occurs, structural response is measured by sensors and immediately collected by the controller. The controller makes optimal decisions for the control devices, which then exert appropriate forces to the structure so that undesired structural vibrations are effectively mitigated. A wireless sensing/control unit can serve as both the sensor and the controller modules of a structural control system. Each wireless unit, in addition to collecting and communicating sensor data in real time, can also make optimal control decisions and command control devices. This section first provides an overview to the prototype wireless structural control system, and then describes the communication protocol design of the system. Laboratory wireless structural control experiments are also reported.
4.1 Overview of the wireless structural control system
Fig. 10 illustrates the communication patterns of a centralized control system using cabled communication and the prototype decentralized structural control system using wireless communication. In a centralized structural control system, one centralized controller collects data from all the sensors in the whole structure, computes control decisions, and
then dispatches command signals to control devices. This centralized control strategy implemented with cabled communication requires high instrumentation cost, is difficult to reconfigure, and potentially suffers from single-point failure at the controller. Wireless decentralized control architectures can offer an alternative solution. In a decentralized architecture, multiple sensors and controllers can be distributively placed in a large structure, where the controller nodes can be closely collocated with the control devices. As each controller only needs to communicate with sensors and control devices in its vicinity, the requirement on communication range can be significantly reduced, and the communication latency decreases by reducing the number of sensors or control devices that each controller has to communicate with.
Fig. 10. Centralized and decentralized control systems.
For application in wireless feedback structural control, real-time communication is important for system performance. Limited wireless communication range poses another challenge while instrumenting a large-scale structure with the wireless sensing and control system. Pariticularly, in discrete-time feedback control, a steady sampling time step and low communication latency are essential for the system performance. The feedback control loop designed for the prototype wireless sensing and control system is illustrated in Fig. 11(a), and the pseudo code implementing the feedback loop is presented in Fig. 11(b). As shown in the figures, sensing is designed to be clock-driven, while control is designed to be event-driven. The wireless sensing nodes collect sensor data at a preset sampling rate, and transmit the data during an assigned time slot. Upon receiving the required sensor data, the control nodes immediately compute control decisions and apply the corresponding command signals to the control devices. If due to occasional data packet loss, a control node doesn’t receive the expected sensor data at one time step, the control node may use a projected data sample for control decisions, or doesn’t take any action at this time step.
4.2 Communication protocol design for the wireless structural control system
Similar to the structural monitoring application, a reliable communication protocol must be properly designed for the wireless structural control system. Fig. 12 illustrates the communication state diagrams of a coordinator unit and other wireless units within a wireless sensing and control subnet. To initiate the system operation, the coordinator unit first broadcasts a start command ‘01StartCtrl’ to all other sensing and control units. Once the start command and its acknowledgement ‘03AcknStartCtrl’ are received, the system starts real-time feedback control operation, i.e. both the coordinator and other units are in State 2.
For illustration purpose, a 3-story structure instrumented with the prototype wireless control system is shown in Fig. 13. The steel frame structure is designed and constructed by researchers affiliated with the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. The prototype wireless system consists of wireless sensors and controllers that are mounted on the structure for measuring structural response data and commanding MR dampers in real-time. Besides the wireless sensing and control units that are necessary for data collection and the operation of the control devices, a remote command server with a wireless transceiver is also included for experimental purpose. In a laboratory setup, the server is designed to initiate the operation of the control system and to log the data flow in the wireless network. To initiate the operation, the command server first broadcasts a start signal to all the wireless sensing and control units. Once the start command is received, the wireless units that are responsible for collecting sensor data start acquiring and broadcasting data at a preset time interval. Accordingly, the wireless units responsible for commanding the MR dampers receive the sensor data, calculate desired control forces, and apply control commands within the specified time interval.
(a) A 3-story test structure mounted on the shake table
(b) Deployment of the wireless sensors, controllers, and control devices
Fig. 13. Laboratory setup of the wireless structural control system.
To coordinate the wireless transmissions during the feedback control, a pre-specified communication sequence should be observed by all the wireless units. For example, if all three wireless control units need velocity data from all the floors to compute control decisions, a communication sequence illustrated in Fig. 14 can be adopted by the prototype system. The control sampling step, which is 80ms in this example, is mostly decided by the total time required for transmitting all four data packets. For the 24XStream wireless transceiver adopted in the system, wireless transmission of each velocity measurement takes about 18ms. During every control time step, the wireless unit C 0 first samples the velocity data V 0 at its own floor, and then sends out the data together with a beacon signal to other
wireless units. Upon receiving the beacon signal, units C 1 , C 2 , and S 3 sequentially broadcast their sensor data. Last, a period of 8ms is designed as a safety cushion for each control sampling time step, allowing certain randomness in the wireless transmission time. The control units C 0 , C 1 , and C 2 compute control decisions and apply actuation signals during the intervals of wireless transmissions.
C 0
C 1
C 2
S 3
18ms 18ms 18ms 18ms 8ms
3ms 12ms 3ms
80ms
Compute
Beacon with data
Data only
Compute
Compute
Fig. 14. Communication sequence in a wireless structural control network.
4.3 Validation experiments for the wireless structural control system
Validation experiments for the wireless control system were conducted at NCREE in Taipei, Taiwan, using the structure shown in Fig. 13. The floor plan of this structure is 3m × 2m, with each floor weight adjusted to 6,000 kg using concrete blocks; inter-story heights are 3m. The three-story structure is mounted on a 5m × 5m 6-DOF shake table. For this study, only longitudinal excitation in one degree of freedom is applied. Besides wireless sensors, a separate set of accelerometers, velocity meters, and linear variable displacement transducers (LVDT) are installed on each floor of the structure; this set of sensors are interfaced to a high-precision tethered data acquisition (DAQ) system native to the NCREE facility.
For this experimental study, three 20 kN MR dampers are installed in a V-brace upon each story of the steel structure (Fig. 13b). The damping coefficients of the MR dampers can be changed by issuing a command voltage between 0V to 1.2V. This command voltage determines the electric current of the electromagnetic coil inside the MR damper, which, in turn, generates a magnetic field that sets the viscous damping properties of the MR damper fluid (Lin, et al. 2005). Two control systems, the wireless control system and a traditional wire-based control system. are installed in the test structure. For the wireless system, a total of four wireless sensors are installed to measure floor velocities (Fig. 13). Velocity feedback control algorithms presented in a previous paper are used by both the wired and the wireless control systems (Wang, et al. 2007b). In a centralized feedback pattern, real- time data from all sensors are required for making the control decisions for every MR damper. For this test structure, the wire-based system can achieve a sampling rate of 200Hz; as shown in Fig. 14, the wireless system can achieve a sampling rate of 12.5Hz.
