APPLICATION OF DECENTRALIZED WIRELESS SENSING AND CONTROL IN CIVIL STRUCTURES

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1 US-Taiwan Workshop on Smart Structural Technology for Seismic Hazar Mitigation Taipei, Taiwan October -4, 006 APPLICATION OF DECENTRALIZED WIRELESS SENSING AND CONTROL IN CIVIL STRUCTURES Yang Wang, R. Anrew Swartz, Jerome P. Lynch 3, Kincho H. Law 4, Kung-Chun Lu 5, Chin-Hsiung Loh 6 ABSTRACT This paper iscusses the potential use of wireless communication an embee computing technologies for structural control applications. Specifically, a prototype wireless structural sensing an control system is escribe. The system incorporates a ecentralize structural control algorithm, which computes control ecisions base on ecentralize output feeback. The performance of this prototype system is valiate in shake table experiments using a half-scale three-story steel structure. The structure is instrumente with three magnetorheological ampers, which are commane by wireless sensing an control units. The experiments also successfully valiate the effectiveness of the ecentralize output feeback control algorithm. Base on this ecentralize control algorithm, a complete set of numerical simulations are conucte using the structural moel of the five-story Kajima-Shizuoka Builing controlle by semi-active hyraulic ampers. The simulation analysis investigates the effects of communication latencies an egrees of centralization on control performance. Both experimental an numerical results show that ecentralize wireless control is viable for future structural control systems. Keywors: structural control, wireless communication, embee computing, ecentralize control, output feeback control. INTRODUCTION Improving the response of structures subjecte to strong ynamic loas continues to be a major challenge in structural engineering. For the past three ecaes, significant research has been conucte in the fiel of structural control (Soong an Spencer, 00). Control evices have been incorporate in structures to mitigate excessive responses (Chu et al., 005). Current structural control systems can be categorize into three major types: (a) passive control (e.g. base isolation), (b) active control (e.g. active mass ampers), an (c) semi-active control (e.g. semi-active variable ampers). Passive control systems, e.g. base isolators, entail the use of passive energy issipation evices to control the response of a structure without the use of sensors an controllers. Active control systems use a small number of large ampers or actuators for irect application of control forces. In a semiactive control system, control evices are use for inirect application of control forces. Semi-active control is currently preferre over active control because it can achieve at least an equivalent level of performance, consumes orers of magnitue less power, an provies higher level of reliability. Examples of semi-active actuators inclue active variable stiffness (AVS) evices, semi-active Ph.D. Caniate, Dept. of Civil an Environmental Engineering, Stanfor Univ., USA, wyang98@stanfor.eu Ph.D. Caniate, Dept. of Civil an Environmental Engineering, Univ. of Michigan, USA, asgar@umich.eu 3 Assistant Professor, Dept. of Civil an Environmental Engineering, Univ. of Michigan, USA, jerlynch@engin.umich.eu 4 Professor, Dept. of Civil an Environmental Engineering, Stanfor Univ., USA, law@stanfor.eu 5 Research Assistant, Dept. of Civil Engineering, National Taiwan Univ., Taipei, Taiwan, r9547@ntu.eu.tw 6 Professor, Dept. of Civil Engineering, National Taiwan Univ., Taipei, Taiwan, lohc00@ntu.eu.tw

2 hyraulic ampers (SHD), electrorheological (ER) ampers, an magnetorheological (MR) ampers. Aitional avantages associate with semi-active control inclue aaptability to real-time excitation, inherent Boune Input/Boune Output (BIBO) stability, an invulnerability against power failure. In a semi-active control system, sensors are eploye in the structure to collect real-time structural response ata uring ynamic excitation. Response ata is then fe into control ecision moules (controllers) to etermine require actuation forces an to apply control commans to system actuators. Commane by control signals, the actuators can generate control forces intene to mitigate unesirable structural responses. In traitional semi-active control systems, coaxial wires are normally use to provie communication links between sensors, actuators an controllers. The installation of a commercial wire-base ata acquisition (DAQ) system can cost up to a few thousan ollars per sensing channel for a typical low-rise builing (Celebi, 00). As the size of the control system grows (efine by the noal ensity an inter-noal spatial istances), cabling neee may result in substantial increases in installation time an expense (Solomon et al., 000). To capitalize on future low-cost semi-active evices that are ensely installe in a structure, wireless communication technology can be employe to eraicate the coaxial cables associate with traitional control systems. Although wireless communication has been extensively explore for use in structural monitoring applications (Straser an Kiremijian, 998; Lynch an Loh, 006a; Wang et al., 006a), few stuies, however, have been reporte on the application to real-time feeback control in structural engineering (Lynch an Tilbury, 005). When replacing wire communication channels with wireless ones for feeback structural control, ifficulties inclue coorination of the wireless noes in a collaborative control network, egraation of real-time performance, an higher probability of ata loss uring transmission. The egraation of the control system s real-time characteristics is a common problem face by istribute network control systems, regarless of using wire or wireless communication (Lian et al., 00). Among the ifferent solutions propose for this problem, one possible remey is the aoption of ecentralize control strategies (Lynch an Law, 00). In a ecentralize control system, the sensing an control network is ivie into multiple subsystems. Controllers are assigne to each subsystem an require only local an neighboring sensor ata to make control ecisions. Therefore, reuce use of the communication channel is offere by ecentralize control architectures, which result in higher control sampling rates. Furthermore, ecentralize control requires relatively shorter communication ranges, enabling more reliable wireless ata transmissions. The rawback of ecentralize control is that ecentralize system architectures may only achieve sub-optimal control performance compare with centralize counterparts, because each subsystem only has local an neighboring sensor ata to calculate control ecisions. This work attempts to investigate the effectiveness of ecentralize wireless sensing an control in civil structures. In this paper, a prototype wireless sensing an control system (Wang et al., 006b) is first introuce. The system consists of multiple stan-alone wireless sensors that form an integrate network through a common wireless communication channel. Each wireless sensor can recor response ata from sensors, calculate control forces, communicate state ata, an comman actuators. Large-scale shake table experiments are conucte on a 3-story steel frame structure installe with MR ampers, in orer to compare the performance of ifferent ecentralize an centralize control schemes. To further examine the issues involve in ecentralize control an communication time elays, numerical simulations are conucte using a structural moel similar to the five-story Kajima-Shizuoka Builing controlle by semi-active hyraulic ampers (SHD) (Kurata et al., 999). A PROTOTYPE WIRELESS STRUCTURAL SENSING AND CONTROL SYSTEM To illustrate the architecture of the prototype wireless sensing an control system, Fig. (a) shows a 3- story structure controlle by three actuators. Wireless sensors an controllers are mounte on the structure for measuring structural response ata an commaning the actuators in real-time. Besies the wireless sensing an control units that are essential for the operation of the control system, a remote ata an comman server with a wireless transceiver is inclue as an optional element

3 S 3 V 3 Floor-3 C i: Wireless control unit (with one wireless transceiver inclue) 3m C S i: Wireless sensing unit (with one wireless transceiver inclue) D V Floor- T i: Wireless transceiver D i: MR Damper 3m C (b) V i: Velocity meter D V Floor- 3m T Lab experiment 0 comman server D 0 C 0 V 0 Floor-0 (a) (c) Figure. Overview to the prototype wireless sensing an control system: (a) a 3-story structure controlle by three actuators; (b) package wireless sensing an control unit ( cm 3 ); (c) printe circuit boar of the signal generation moule ( cm ). responsible for logging the flow of wireless ata. During an experimental test, the comman server first notifies the wireless sensing an control units to initiate automate operations. Once the start comman is receive, the wireless units that are responsible for collecting sensor ata start acquiring an broacasting ata at a specifie time interval. Accoringly, the wireless units responsible for commaning the actuators receive the sensor ata, calculate esire control forces in real-time, an apply control commans at the specifie time interval. The wireless unit is esigne in such a way that the unit can serve as either a sensing unit, or a control unit, or a unit for both sensing an control. This flexibility is supporte by an integrate harware esign base upon a wireless sensing unit (Fig. b) previously propose for wireless structural monitoring by Wang et al. (006a). The three original functional moules inclue in the wireless sensing unit esign are the sensor signal igitizer, the computational core, an the wireless transceiver. To exten the functionality of the wireless sensor for actuation, an off-boar control signal generation moule (Fig. c) is esigne an fabricate. The control signal generation moule consists of a single-channel 6-bit igital-to-analog converter an other support electronics. The moule can output an analog voltage from -5V to 5V at rates as high as 00 khz. Detaile esign of the wireless sensing an control unit an the control signal generation moule has been escribe by Wang et al. (006b). CENTRALIZED AND DECENTRALIZED TIME-DELAY CONTROL ALGORITHMS USING VELOCITY FEEDBACK In this stuy, a linear quaratic regulator (LQR) output feeback control algorithm is employe for the prototype wireless structural sensing an control system. The algorithm can be briefly summarize as follows. For a lumpe-mass structural moel with n egrees-of-freeom (DOF) an m actuators, the system state-space equations consiering l time steps of elay can be state as: z [ k+ ] = A z [ k] + B p [ k l], where z [ k] [ k] [ k] x = x& () Here z [ k] represents the n iscrete-time state-space vector, [ k l] p is the elaye m control force vector, A is the n n system matrix (containing the information about structural mass, stiffness, an amping), an B is the n m actuator location matrix. The primary objective

4 of the time-elay LQR problem is to minimize a cost function J by selecting an optimal control force trajectory p : T T ( [ ] [ ] [ ] [ ]) J = z k Qz k + p k l Rp k l, where Q 0 an R > 0 () p k= l n n m m In an output feeback control esign, control ecisions are compute base on real-time ata in the q system output vector y [ k]. The output vector is efine by a q n linear transformation, D, to z k : the state-space vector [ ] [ k] = [ k] y D z (3) For example, if the relative velocities on all floors (but not the relative isplacements) are measurable, D can be efine (by letting q = n) as: [ ] D = 0 I (4) n n n n In another example, if inter-story velocities between ajacent floors are measurable, the output matrix D can be efine (by letting q = n) as: 0 0 L 0 0 L 0 D = [ 0n n] 0 L 0 M O O O M 0 L 0 n n (5) The m q optimal gain matrix G is esigne to provie the optimal output feeback control force: [ k] = [ k] p G y (6) Chung et al. (995) propose the formulation to the above output feeback control problem consiering time elay (l time steps). As a result, a set of couple nonlinear matrix equations can be solve for an optimal output feeback gain matrix G. In our implementation, an iterative algorithm put forth by Lunze (990) is moifie to solve the matrix equations (Wang et al., 006c). The algorithm escribe by Lunze (990) also has the flexibility to hanle aitional external constraints. In particular, this algorithm computes an optimal control solution for a ecentralize system simply by constraining the structure of G to be consistent with the ecentralize architecture. For example, the following equations illustrate the constraine structures of two ecentralize output feeback gain matrices for a 3-story lumpe-mass structure (n = 3): * 0 0 * * 0 G 0 * 0, 0 * * = G = (7) 0 0 * 0 * * When combine with the output matrix D efine in Eq. (4) or (5), the pattern in G specifies that when computing control ecisions, the actuator at each floor only nees the entry in the output vector y that correspons to this floor. The pattern in G specifies that the control ecisions also require information from a neighboring floor.

5 EXPERIMETAL VALIDATION TESTS USING THE PROTOTYPE WIRELESS STRUCTURAL SENSING AND CONTROL SYSTEM To stuy the potential application of the wireless sensing an control system for ecentralize structural control, valiation tests on a 3-story frame structure instrumente with MR ampers are conucte at the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. This section first introuces the experimental setup, an then presents the test results. Valiation Test Setup A three-story steel frame structure is esigne an constructe by researchers affiliate with NCREE (Fig. a). The floor plan of this structure is 3m m, with each floor weight ajuste to 6,000 kg using concrete blocks. The inter-story height is 3m. Both the beams an the columns of the structure are constructe with H steel I-beam elements. The three-story structure is mounte on a 5m 5m 6-DOF shake table. The shake table can generate groun excitations with frequencies spanning from 0.Hz to 50Hz. For this stuy, only longituinal excitations are use. Along this irection, the shake table can excite the structure with a maximum acceleration of 9.8m/s. The excitation has a maximum stroke an force of ±0.5m an 0kN, respectively. The test structure is heavily instrumente with accelerometers, velocity meters, an linear variable isplacement transucers (LVDT) installe on each floor of the structure to measure the ynamic response. These sensors are interface to a high-precision wire-base ata acquisition (DAQ) system native to the NCREE facility; the DAQ system is set to a sampling rate of 00 Hz. A separate set of wireless sensors are installe as part of the wireless control system. For this experimental stuy, three 0 kn MR ampers are installe with V-braces on each story of the steel structure (Fig. b). The amping coefficients of the MR ampers can be change by issuing a comman voltage between 0V to.v. This comman voltage etermines the electric current of the electromagnetic coil in the MR amper, which in turn, generates a magnetic fiel that sets the viscous amping properties of the MR amper. Calibration tests are first conucte on the MR ampers before mounting them to the structure so that moifie Bouc-Wen amper moels can be formulate for each amper (Lin et al., 005). In the real-time feeback control tests, hysteresis moel parameters for the MR ampers are an integral element in the calculation of amper actuation voltages. Fig. (c) illustrates a wireless control unit an an off-boar control signal generation moule that work together to comman an MR amper. (b) (a) (c) Figure. Laboratory setup: (a) the 3-story test structure mounte on the shake table; (b) the MR amper installe between the st floor an the base floor of the structure; (c) a wireless control unit an an off-boar control signal generation moule.

6 Table. Different ecentralization patterns an sampling time for the wireless an wire-base control experiments. Wireless System Wire System Degree of Centralization 3 3 Gain Matrix Pattern G in Eq. (7) G in Eq. (7) N/A N/A Output Matrix D in Eq.(5) D in Eq.(5) D in Eq.(4) D in Eq.(4) Sampling Time/Rate 0.0s / 50Hz 0.06s / 6.67Hz 0.08s /.5Hz 0.005s / 00Hz For the wireless system, a total of four wireless sensors are installe, following the eployment shown in Fig. (a). Each wireless sensor is interface to a Tokyo Sokushin VSE5-D velocity meter to measure the absolute velocity response for each floor of the structure as well as the base. The sensitivity of this velocity meter is 0V/(m/s) with a measurement limit of ± m/s. The three wireless sensors on the first three levels of the structure (C 0, C, an C ) are also responsible for commaning the MR ampers. Besies the wireless control system, a traitional wire-base control system is installe in the structure for comparative tests. Centralize an ecentralize velocity feeback control schemes are use for the wire an the wireless control systems. Table summarizes the ifferent patterns of the gain matrix G, the output matrices D, an the achievable sampling times for the centralize, partially ecentralize an fully ecentralize control strategies (which are enote by egrees of ecentralization, 3, an, respectively). For this test structure, the wire-base system can achieve a sampling rate of 00Hz, or a time step of 0.005s. Mostly ecie by the communication latency of the 4XStream wireless transceivers, the wireless system can achieve a sampling rate of.5hz (or a time step of 0.08s) for the centralize control scheme. This sampling rate is ue to each wireless sensor waiting in turn to communicate its ata to the network (about 0.0s for each transmission). An avantage of the ecentralize architecture is that fewer communication steps are neee, thereby reucing the time for wireless communication. Experimental Results To ensure that appropriate control ecisions are compute by the wireless control units, one necessary conition is that the real-time velocity ata use by the control units are reliable. Rarely experiencing ata losses uring the experiments, our prototype wireless sensor network proves to be robust. In case ata loss happens, the wireless control unit is currently esigne to simply use a previous ata sample. For the experimental results presente herein, the groun excitation is the 940 El Centro NS earthquake recor (Imperial Valley Irrigation District Station) scale to a peak groun acceleration of m/s. To illustrate the performance of ifferent ecentralize schemes with ifferent communication latencies, the same groun excitation is applie to the original uncontrolle structure an the structure 3 Maximum Inter-story Drifts No Control Wireless # Wireless # Wireless #3 Wire 3 Maximum Absolute Accelerations No Control Wireless # Wireless # Wireless #3 Wire Story Floor Drift (m) Acceleration (m/s ) (a) (b) Figure 3. Experimental results of ifferent control schemes using the El Centro excitation scale to a peak acceleration of m/s : (a) peak inter-story rifts; (b) peak accelerations.

7 controlle by the four ifferent wireless an wire control schemes as efine in Table. Fig. 3 illustrates the structure s peak inter-story rifts an floor accelerations uring these experimental runs. Compare with the uncontrolle structure, all wireless an wire control schemes achieve significant reuction with respect to maximum inter-story rifts an absolute accelerations. Among the four control cases, the wire centralize control scheme shows better performance in achieving the least peak rifts an secon least overall peak accelerations. This result is rather expecte, because the wire system has the avantages of lower communication latency an utilizes complete sensor ata from all floors. The wireless schemes, although running at longer sampling steps, achieve control performance comparable to the wire system. The fully ecentralize wireless control scheme (case #), results in uniform peak inter-story rifts an the least peak floor accelerations. This illustrates that in the ecentralize wireless control cases, the higher sampling rate (from lower communication latency) can potentially compensate the loss of ata from ignoring the sensor ata from faraway floors. NUMERICAL SIMULATIONS INVESTIGATING EFFECTS OF COMMUNICATION LATENCIES AND DEGREES OF CENTRALIZATION With the ecentralize output feeback control algorithm valiate through experiments, further analysis of the ecentralize control strategies is investigate with numerical simulations. A five-story moel similar to the Kajima-Shizuoka Builing is employe (Kurata et al., 999). The steel-structure builing has a total height of about 9m (Fig. 4a). For the simulations, two semi-active hyraulic ampers (SHD) are installe at each floor. In the numerical simulations, it is assume that both the inter-story isplacement an inter-story velocity relative to the lower floor are measurable. Similarly, the state-space system is formulate such that the state-space vector contains inter-story isplacements an velocities, rather than the isplacements an velocities relative to the groun. A n n output matrix D is efine to reflect this requirement on sensor ata. The simulations are conucte for ifferent egrees of centralization (DC), as illustrate in Fig. 4(a); the case where DC equal to i represents that the neighboring i floors form an information group an share their sensor ata. For example, the case where DC= implies each group consists of only one floor. For the case where DC=3, each group consists of three floors, resulting in 3 information groups for the 5 story builing. For DC=5, all 5 floors share their sensor ata, resulting in a centralize information architecture. Base on the above efinitions for output matrix D an egrees of centralization, the gain matrix consists of two block submatrices with the same symmetric shape constraints. In each block submatrix, the iagonal entries an the j th (j =,.., i- ) entry above an below the iagonal entry are non-zero. For example, when DC =, the gain matrix has the following shape constraint: Floor-5 & x( t) 4.m 4 x 3.6 m Floor- DC = DC = 3 DC = 5 (a) SHD p(t) Maximum Control Force Maximum Displacement Stiffness of the SHD Maximum Damping Coefficient Minimum Damping Coefficient Maximum Shaft Velocity Power Consumption (b),000 kn +/- 6 cm 400,000 kn/m 00,000 kn-s/m,000 kn-s/m 5 cm/s 70 Watts Figure 4. A five-story moel similar to the Kajima-Shizuoka Builing: (a) sie elevation of the builing an information group partitioning for ifferent egrees of centralization (DC); (b) key parameters of the SHD amper.

8 G * * 0 L 0 * * 0 L 0 * * * O 0 * * * O 0 = 0 * * O 0 0 * * O 0 M O O O * M O O O * 0 0 * * 0 0 * * L L n n (8) The left submatrix an the right submatrix correspon to the isplacement part an the velocity part, respectively, of the output vector y. As in the Shizuoka builing, each SHD amper is moele to have a maximum force of,000kn (Fig. 4b). The amping force of the SHD amper can be regulate by changing the opening rate of the hyraulic flow control valve. If the esire amper force is in an opposite irection to the inter-story velocity, as shown in Fig. 4(b), the amping coefficient is ajuste so that a amper force closest to the esire force is generate. If the esire force is in the same irection to the inter-story velocity, the amping coefficient is set to its minimum value. A SHD amper is installe as a V-brace on each floor. To accurately moel the amping force, the Maxwell element propose by Hataa et al. (000) is employe. In a Maxwell element, a ashpot an a stiffness spring are connecte in series, which result in a amping force escribe by the following ifferential equation: keff pt &() + pt () = keff xt &() (9) c () t SHD where p(t) an & x() t enote the amping force an the inter-story velocity, respectively, k eff represents the effective stiffness of the amper in series with the V-brace, an cshd () t is the ajustable amping coefficient of the SHD amper. Various combinations of centralization egrees ( through 5) an sampling time steps ranging from 0.005s to 0.s (at a resolution of 0.005s) are simulate. Aitionally, four groun motion recors are use for each simulation: the 940 El Centro NS recor (Imperial Valley Irrigation District Station) scale to a peak groun acceleration (PGA) of m/s, the same 940 El Centro NS recor scale to a PGA of m/s, the 999 Chi-Chi NS recor (TCU-076 Station) scale to a PGA of m/s, an the 995 Kobe NS recor (JMA Station) scale to a PGA of m/s. Performance inices propose by Spencer et al. (998) are aopte. In particular, two representative performance inices employe are: PI max () t i ti, = max Earthquakes max ˆ i () t ti, J LQR, an PI = max Earthquakes Jˆ LQR (0) Here PI an PI are the performance inices corresponing to inter-story rifts an LQR cost inices, respectively. In Eq. (0), i () t represents the inter-story rift between floor i (i =,, n) an its lower floor at time t, an max i () t is the maximum inter-story rift over the entire time history an ti, among all floors. The maximum inter-story rift is normalize by its counterpart max ˆ i () t, which is ti, the maximum response of the uncontrolle structure. The largest normalize ratio among the simulations for the four ifferent earthquake recors is efine as the performance inex PI. Similarly, the performance inex PI is efine for the LQR control inex J LQR, as given in Eq. (). When computing the LQR inex over time, a uniform time step of 0.00s is use to collect the structural response ata points, regarless of the sampling time step of the control scheme; this allows one control strategy to be compare to another without concern for the ifferent sampling time steps use in the control solution.

9 Values of the two control performance inices are plotte in Fig. 5 for ifferent combinations of egrees of centralization an sampling time steps. The plots shown in Fig. 5(a) an 5(b) illustrate that the egrees of centralization an sampling steps have significant impact on the performance of the control system. Generally speaking, control performance is better for higher egrees of centralization an shorter sampling times. To better review the simulation results, the performance inices for the five ifferent control schemes are re-plotte as a function of sampling time in Fig. 5(c) an 5(). As shown in Fig. 5(c), if a partially ecentralize control system with DC equal to can achieve 0.0s sampling step an a centralize system can only achieve 0.03s ue to aitional communication latency, the partially ecentralize system can result in much lower maximum inter-story rifts. Similar trens are observe in Fig. 5(), except that the plots are smoother ue to the summation process for computing the LQR inices. CONCLUSIONS In this stuy, a prototype wireless structural sensing an control system is evelope, an its performance valiate in real-time feeback control experiments using a 3-story steel structure instrumente with MR ampers. Simulation analysis is conucte using a 5-story builing structure instrumente with SHD ampers, by varying the egrees of centralization an the sampling time steps of the control system. Both the experimental results an the simulations results illustrate that for wireless sensing an control, ecentralize control strategies may provie equivalent or even superior control performance, given that their centralize counterparts coul suffer longer sampling steps ue to wireless communication latencies. ACKNOWLEDGMENTS This research is partially fune by the National Science Founation uner grants CMS Maximum Drift Among All Stories LQR Inex Performance Inex PI Samping Time(s) 0 (a) Degree of Centralization Performance Inex PI Samping Time(s) 0 (b) Degree of Centralization Performance Inex PI Maximum Drift Among All Stories 0.6 DC = DC = DC = DC = 4 DC = Samping Time (s) (c) Performance Inex PI DC = DC = DC = 3 DC = 4 DC = 5 LQR Inex Samping Time (s) () Figure 5. Simulation results illustrating performance inexes for ifferent sampling time steps an egrees of centralization (DC): (a) 3D plot for performance inex PI ; (b) 3D plot for performance inex PI ; (c) conense D plot for PI ; () conense D plot for PI.

10 (Stanfor University), CMS (University of Michigan), an the Office of Naval Research Young Investigator Program aware to Prof. Lynch at the University of Michigan. Aitional support is provie by National Science Council in Taiwan uner Grant No. NSC Z The authors wish to thank the two fellowship programs: the Office of Technology Licensing Stanfor Grauate Fellowship an the Rackham Grant an Fellowship Program at the University of Michigan. The authors woul also like to acknowlege the travel supports provie by the National Science Founation for attening the workshop. REFERENCES Celebi, M. (00). Seismic Instrumentation of Builings (with Emphasis on Feeral Builings), Report No , Unite States Geological Survey (USGS), Menlo Park, CA, USA. Chu, S.Y., T.T. Soong, an A.M. Reinhorn, (005). Active, Hybri an Semi-active Structural Control, John Wiley & Sons Lt, West Sussex, Englan. Chung, L.L., C.C. Lin, an K.H. Lu (995). Time-elay Control of Structures, Earthquake Eng. Struct. Dyn., John Wiley & Sons, 4(5), Hataa, T., T. Kobori, M. Ishia, an N. Niwa (000). Dynamic Analysis of Structures with Maxwell Moel, Earthquake Eng. Struct. Dyn., John Wiley & Sons, 9(), Kurata, N., T. Kobori, M. Takahashi, N. Niwa, an H. Miorikawa (999). Actual Seismic Response Controlle Builing with Semi-active Damper System, Earthquake Eng. Struct. Dyn., John Wiley & Sons, 8(), Lian, F.-L., J. Moyne, an D. Tilbury (00). Network Design Consieration for Distribute Control Systems, IEEE T. Contr. Syst. T., IEEE, 0(), Lin, P.-Y., P.N. Roschke, an C.-H. Loh (005). System Ientification an Real Application of a Smart Magneto-Rheological Damper, Proc. of the 005 Intl. Symposium on Intelligent Control, Limassol, Cyprus, June 7-9, 005. Lunze, J. (99). Feeback Control of Large-scale Systems, Prentice Hall, Hertforshire, UK. Lynch, J.P. an K.H. Law (00). Decentralize Control Techniques for Large Scale Civil Structural Systems, Proc. of the 0 th Int. Moal Analysis Conf., Los Angeles, CA, February 4-7, 00. Lynch, J.P. an K. Loh (005). A Summary Review of Wireless Sensors an Sensor Networks for Structural Health Monitoring, Shock Vib. Dig., Sage Publications, 38(), 9-8. Solomon, I., J. Cunnane, an P. Stevenson (000). Large-scale Structural Monitoring Systems, Proc. of SPIE Non-estructive Evaluation of Highways, Utilities, an Pipelines IV, Newport Beach, CA, March 7-9, 000. Soong, T.T. an B.F. Spencer, Jr. (00). Supplemental Energy Dissipation: State-of-the-art an State-of-thepractice, Eng. Struct., Elsevier, 4(3), Spencer, B.F., Jr., R.E. Christenson, an S.J. Dyke, (998). Next Generation Benchmark Control Problem for Seismically Excite Builings, Proc. of n Worl Conf. on Structural Control, Kyoto, Japan, June 9 - July, 998. Straser, E.G. an A.S. Kiremijian (998). A Moular, Wireless Damage Monitoring System for Structures, Report No. 8, John A. Blume Earthquake Eng. Ctr., Stanfor University, Stanfor, CA, USA. Wang, Y., J.P. Lynch, an K.H. Law (006a). A Wireless Structural Health Monitoring System with Multithreae Sensing Devices: Design an Valiation, Struct. Infrastructure Eng., Taylor & Francis, in press. Wang, Y., A. Swartz, J.P. Lynch, K.H. Law, K.-C. Lu, an C.-H. Loh (006b). Wireless Feeback Structural Control with Embee Computing, Proc. of the SPIE th Intl. Symposium on Nonestructive Evaluation for Health Monitoring an Diagnostics, San Diego, CA, USA, February 6 - March, 006. Wang, Y., R.A. Swartz, J.P. Lynch, K.H. Law, K.-C. Lu, an C.-H Loh (006c). Decentralize Civil Structural Control using a Real-time Wireless Sensing an Control System, Proc. of the 4th Worl Conf. on Structural Control an Monitoring, San Diego, CA, USA, July - 3, 006.