DECENTRALIZED REAL-TIME VELOCITY FEEDBACK CONTROL OF STRUCTURES USING WIRELESS SENSORS

Transcription

1 4th International Conference on Earthquake Engineering Taipei, Taiwan October -3, 6 Paper No. 7 DECENTRALIZED REAL-TIME VELOCITY FEEDBACK CONTROL OF STRUCTURES USING WIRELESS SENSORS Yang Wang, R. Anrew Swartz, Jerome P. Lynch 3, Kincho H. Law 4, Kung-Chun Lu 5, Chin-Hsiung Loh 6 ABSTRACT Substantial research has been conucte to avance structural control as a means of mitigating the ynamic response of civil structures. Recently, the structural engineering fiel has begun exploring low-cost wireless sensors for structural monitoring applications. Wireless sensors can be employe to reuce the labor an costs associate with installing extensive lengths of coaxial wires in toay s structural control systems. In this stuy, the wireless sensors are esigne to perform four major tasks in a control system: (a) collect real-time structural response ata; (b) wirelessly transmit or receive response ata; (c) process ata an compute control ecisions; an () apply control signals to structural actuators. The emans of the control system to respon in real-time pose as a challenge for wireless sensing an control, ue to communication elays between wireless sensors an possible ata loss. This paper investigates the feasibility of employing ecentralize an partially ecentralize control strategies to reuce communication latencies associate with wireless sensor networks. Control algorithms are embee in a wireless sensor prototype esigne for use in a structural control system. Both numerical simulation an experimental results show that ecentralize wireless control is viable for future structural control systems, especially wireless ones. Keywors: structural control, wireless communication, embee computing, ecentralize control, velocity feeback control INTRODUCTION Over the past few ecaes, structural control technology has emerge as an effective metho for mitigating structural responses an reucing amage uring strong ynamic excitations (Soong an Spencer, ; Chu et al., 5). Current structural control technology can be classifie using three categories: (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 has the avantage of energy efficiency, an active control has the avantage of being aaptable to real-time excitations. As a hybri between passive an active control, semi-active control effectively combines these two avantages. In a semi-active control system, sensors are installe in a structure to recor real-time structural response ata. Base on the response ata, a controller executes an embee algorithm in real-time to etermine an optimal set of forces that mitigate the response of the structure. The controller then issues commans to the semi-active evices installe, which in turn apply esire 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@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, lohc@ntu.eu.tw

2 forces to the structure. Examples of semi-active actuators inclue active variable stiffness (AVS) evices, semi-active hyraulic ampers (SHD), electrorheological (ER) ampers, an magnetorheological (MR) ampers. Since semi-active control evices issipate energy from the system, they have the aitional avantage of renering the control system boun input/boune output (BIBO) stable. In traitional semi-active or active control systems, coaxial wires are normally use to provie communication links between sensors, actuators an controllers. The installation costs associate with tethere communication channels can be as high as a few thousan ollars per channel (Celebi, ). As structural size grows an the number of actuation an sensing noes in the control system rises, the time an costs neee to install a wire monitoring or control systems increase significantly (Solomon et al., ). To capitalize on future low-cost semi-active evices that may be installe in high ensity, wireless communication technology can be aopte to eliminate the coaxial wires associate with traitional control systems. Wireless communication channels can be use for feeing real-time sensor ata to the controllers an for issuing control commans from the controllers to the actuators. In structural health monitoring, wireless communication has been shown sufficiently reliable for collecting structural sensor ata (Straser an Kiremijian, 998; Lynch an Loh, 5). The authors have recently explore the possibility of employing wireless communication technologies for feeback structural control, an evelope a prototype real-time wireless sensing an control system for civil structures (Wang et al., 6a). The system consists of multiple stan-alone wireless sensors an controllers that form an integrate wireless network through a common-use wireless communication channel. Utilizing moern embee computing technology, istribute low-cost wireless controllers are closely associate with the structural actuators. Experimental tests have been successfully carrie out to valiate the feasibility of this system. When replacing wire communication channels with wireless ones for feeback structural control, ifficulties inclue the coorination of wireless noes in a collaborative network, egraation of realtime performance, an higher probability of ata loss uring transmission (Ploplys, 4). Among ifferent solutions to the egraation of the control system s real-time characteristics, one efficient remey for this problem is the aoption of ecentralize control strategies (Lynch an Law, ). In a ecentralize control system, the sensing an control network is ivie into multiple subsystems. Controllers are assigne to each subsystem an require only subsystem sensor ata for control ecisions. Therefore, reuce use of the communication channel is offere by a ecentralize control architecture, which results in higher maximum permissible sampling rates. Meanwhile, ecentralize control requires relatively shorter communication ranges, enabling more reliable ata transmissions. The rawback of ecentralize control is that ecentralize control architectures may only achieve sub-optimal control performance compare with their centralize counterparts because each controller has sensor ata only from its subsystem from which to make control ecisions. This stuy attempts to investigate the effects of communication latencies in centralize an ecentralize control strategies. In this paper, a prototype wireless sensing an control system is first introuce, followe by a brief escription of centralize an ecentralize output feeback control algorithms. Numerical simulation results show that the higher sampling rates in ecentralize control may compensate for the isavantage of only having incomplete state information for etermining control ecisions. Large-scale shake table experiments are conucte on a 3-story steel frame test structure installe with MR ampers to compare the performance of ifferent ecentralize an centralize control schemes. A PROTOTYPE REAL-TIME WIRELESS 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 MR ampers. Wireless sensors an controllers are mounte on the structure for measuring structural response ata an commaning the MR 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 Ci: Wireless control unit (with one wireless transceiver inclue) S3 V3 Floor-3 Si: Wireless sensing unit (with one wireless transceiver inclue) 3m C Ti: Wireless transceiver D V Di: MR Damper Floor- Vi: Velocity meter Floor plan: 3m x m Floor weight: 6,kg Steel I-section beams an columns: H5 x 5 x 7 x Lab experiment comman server T 3m 3m D D C C V V Floor- Floor- (b) (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 actuation 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 specifie time intervals. 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 their specifie time intervals. The wireless unit is esigne in such a way that the unit can serve as either a sensing unit, 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. (6b). 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 khz. Detaile esign of the wireless sensing an control unit an the control signal generation moule has been escribe by Wang et al. (6a). CENTRALIZED AND DECENTRALIZED LINEAR OUTPUT FEEDBACK CONTROL ALGORITHMS CONSIDERING TIME-DELAY This section reviews briefly the basic formulation of linear quaratic regulator (LQR) output feeback control. Strategies for hanling communication time elays an constraints ue to ecentralize control are iscusse. Numerical simulations are conucte to evaluate the effects of communication latency for centralize an ecentralize control strategies. Formulation for Linear Output Feeback Control The output feeback iscrete-time LQR control solution can be briefly summarize as follows. For a lumpe-mass structural moel with n egrees-of-freeom (DOF) an m actuators, the system iscretetime 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 ()

4 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 information about structural mass, stiffness an amping), an B is the n m actuator location matrix. The primary objective 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 an R > () p k= l n n m m In an output feeback control esign, when control ecisions are compute, only ata in the system y k are available. The output vector is efine by a q n linear transformation,, output vector [ ] to the state-space vector [ k] z : [ k] = [ k] y D z (3) For example, if only the relative velocities on all floors (but not the relative isplacements) are measurable, can be efine as: D [ ] D = I (4) _cen n n n n In another example, if inter-story velocities between ajacent floors are measurable, the following output matrix can be use: D D _ec = (5) D The m q optimal gain matrix G is esigne to provie the linear output feeback control force: [ k] = [ k] p G y (6) Chung et al. (995) propose a solution to the above output feeback control problem consiering time elay (l time steps). An equivalent system to the original ifference equations (Eq. ) can be obtaine by properly efining the augmente matrices an vectors (enote here with over bars) as: [ k + ] = [ k] + [ ] z Az Bp k (7) As a result, the following nonlinear couple matrix equations can be obtaine: ( T T T + ) ( + ) + ( + ) ( A + ) ( + ) T BGD L A BGD L+ Zl T T B H( A + BGD) LD + RGDLD A BGD HA BGD H Q D G RGD = (8a) = (8b) T = (8c) The above equations are then solve for an optimal output feeback gain matrix G, the Lagrangian matrix, L, an the Hamiltonian matrix, H. Detaile erivations have been iscusse by Chung et al. (995). In our implementations, an iterative algorithm put forth by Lunze (99) is moifie to solve the above matrix equations (Wang et al., 6c). The iterative algorithm can also be applie to

5 compute an optimal control solution for a ecentralize system simply by constraining the structure of G to be consistent with the ecentralize architecture. The following equation illustrates the patterns of two ecentralize output feeback gain matrices for a simple 3-story lumpe-mass structure: G * * * = *, G = * * * * * _ec _ec (9) The pattern in G specifies that when computing control ecisions, the actuator on each floor only _ec nees the entry in the output vector y that correspons to that floor. The pattern in the control ecisions also require information from a neighboring floor. Simulation Results using Centralize an Decentralize Control Strategies G _ec specifies Numerical simulations have been conucte to assess the performance of ecentralize an centralize control strategies consiering time elays ue to communication latency. A numerical moel for the 3-story half-scale laboratory structure illustrate in Fig. (a) is use for the simulation. For simplicity, one ieal structural actuator, which is capable of proucing any esire force uner the maximum limit of kn, is eploye between every two ajacent floors. Three control architectures are employe: () ecentralize, () partially ecentralize, an (3) centralize control. Different patterns of the gain matrices, G, an the output matrices, D, for these three control architectures are summarize in Table. As efine by these matrices, three centralize an ecentralize velocity feeback patterns are aopte. An LQR weighting matrix Q minimizing inter-story rifts over time an a iagonal weighting matrix R are use when esigning the optimal gain matrices for all the simulations presente herein. Various combinations of system architectures (centralize, partially ecentralize an fully ecentralize) an sampling time steps (ranging from.5s to.s at a resolution of.5s) are simulate. To assess the performance of each control scheme, three groun motion recors are use for the simulation: the El Centro (94), the Kobe (995), an the Chichi (999) earthquake recors. Performance inices propose by Spencer et al. (998) are aopte. In particular, two representative performance inices employe here are: PI max () t i ti, = max El Centro ˆ, PI Kobe max i () t Chichi ti, J LQR = max Jˆ LQR El Centro Kobe Chichi () where PI an PI are the performance inices corresponing to inter-story rifts an LQR control measure, respectively. In Eq. (), t represents the inter-story rift between floor i (i =,, 3) i () ti, ( ) an the floor below (i-) at time t, an max i t is the maximum inter-story rift over the entire time history an among all three floors. The maximum inter-story rift is normalize by its counterpart max ˆ i () t, the maximum response of the uncontrolle structure. The largest normalize ratio among ti, the simulations for the three ifferent earthquake recors is efine as the performance inex PI. Table. Different ecentralization patterns for the control simulations an experiments. Degree of Centralization () Decentralize () Partially Decentralize (3) Centralize Gain Matrix Constraint G _ec in Eq. (9) G _ec in Eq. (9) N/A Output Matrix D _ec in Eq. (5) D _ec in Eq. (5) D _cen in Eq. (4)

6 Similarly the performance inex PI is efine for the LQR control inex J LQR, as given in Eq. (); again, the cost inex is normalize by the cost inex corresponing to the uncontrolle structure. When computing the LQR inex over time, a uniform time step of.5s is use to collect the structural response ata points, regarless of the sampling time step of the control scheme. Values of the two control performance inices are plotte in Fig. for ifferent combinations of egree of centralization ( = ecentralize; = partially ecentralize; 3 = centralize) an sampling time steps. The plots shown in Fig. (a) an (b) illustrate that egree of centralization an sampling step have important effects on the overall control performance. Generally speaking, control performance is better for higher egrees of centralization an for shorter sampling steps. To better review the simulation results, the performance inices for the three ifferent control schemes are replotte as a function of sampling time in Fig. (c) an (). As shown in Fig. (c), if a partially ecentralize control system can achieve.4s sampling step an a centralize system can only achieve.8s ue to aitional communication latency, the partially ecentralize system can result in lower maximum inter-story rift. Similar trens are observe in Fig. (), although for a given sampling time step, the performance inex PI for the centralize case is always lower than the inices for the two ecentralize cases. EXPERIMETAL VALIDATION TESTS USING A REAL-TIME WIRELESS STRUCTURAL SENSING AND CONTROL SYSTEM To stuy the potential use of the wireless sensing an control system for ecentralize structural control, valiation tests are conucte at the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both a traitional wire control system (for baseline comparisons) an a wireless sensing an control system are employe to implement the real-time feeback control of a 3- story steel frame instrumente with three MR ampers. Maximum Drift Among Three Stories LQR Inex e xpi e In n c a rm P erfo Samping (a) 3 Degree of Centralization e xpi e In n c a rm P erfo.5..5 Samping (b) 3 Degree of Centralization e xpi e In n c a rm P erfo Maximum Drift Among Three Stories - Decentr. - Partially Decentr. 3 - Centr. e xpi e In n c a rm P erfo LQR Inex - Decentr. - Partially Decentr. 3 - Centr Samping (c) Samping () Figure. Simulation results illustrating control performance inexes for ifferent sampling time steps an centralization egrees: (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.

7 Valiation Test Setup A three-story steel frame structure is esigne an constructe by researchers affiliate with NCREE (Fig. 3a). The imensions of the structure are provie as shown in Fig. (a). The three-story structure is mounte on a 5 m 5 m 6-DOF shake table. The shake table can generate groun excitations with frequencies spanning from. Hz to 5 Hz. For this stuy, only longituinal excitations are use. Along this irection, the shake table can excite the structure with a maximum acceleration of 9.8 m/s. The excitation has a maximum stroke an force of ±.5 m an kn, 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 fixe at a sampling rate of Hz. A separate set of wireless sensors are installe as part of the wireless control system. For this experimental stuy, three kn MR ampers are installe with V-braces on each story of the steel structure (Fig. 3b). The amping coefficients of the MR ampers can be change by issuing a comman voltage between V 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., 5). 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. 3(c) illustrates a wireless control unit an an off-boar control signal generation moule that work together to comman an MR amper. 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 V//s) with a measurement limit of ± m/s. The three wireless sensors on the first three levels of the structure (C, 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 escribe earlier (Table ) are use for both the wire an the wireless control (b) (a) (c) Figure 3. 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.

8 Table. Different ecentralization patterns an sampling steps for the wireless an wire-base control experiments (egrees of centralization are efine as shown in Table ). Wireless System Wire System Degree of Centralization 3 3 Sampling Step/Rate.s / 5Hz.6s / 6.67Hz.8s /.5Hz.5s / Hz systems. As shown in Table, ifferent ecentralization patterns an sampling steps are teste. For the test structure, the wire-base system can achieve a sampling rate of Hz, or a time step of.5s. Mostly ecie by the communication latency of the 4XStream wireless transceivers integrate with each wireless sensor, the wireless system can achieve a sampling rate of.5hz (or a time step of.8s) 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.s 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 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 use a previous ata sample. To illustrate the reliability of the velocity ata collecte an transmitte by the wireless units, Fig. 4(a) presents the Floor- (Fig. (a)) time history ata uring a centralize wireless control test, as collecte separately by the cable DAQ system an recore by the three wireless control units. The groun excitation applie is the El Centro (94) NS earthquake recor scale to a peak groun acceleration of m/s. During the test, unit C measures the ata from the associate velocity meter irectly, stores the ata in its own memory bank, an transfers the ata wirelessly to unit C an C. After the test run / s ) y t c i V el o. Floor- Absolute Velocity Measure by Cable System s ) y / Recore by Unit C t c i -. V el o s ) y / Recore by Unit C t c i -. V el o s ) Recore by Unit C y / t c i V el o (a) ) D ri f t ) D ri f t ) D ri f t Floor 3/ Inter-Story Drift uner El Centro Excitation (Peak m/s ). Uncontrolle Structure. Wireless Centralize Cable Control Floor / Inter-Story Drift uner El Centro Excitation (Peak m/s ) Floor / Inter-Story Drift uner El Centro Excitation (Peak m/s ) (b) Figure 4. Experimental time histories for: (a) cable an wireless sensor ata; (b) inter-story rifts of the structure with an without control.

9 is complete, ata from all three control units are sequentially streame to the experiment comman server, where the results are plotte as shown in Fig. 4(a). These plots illustrate strong agreements among ata recore by the three wireless control units an by the cable system using a separate set of velocity meters an ata acquisition system. This result illustrates that the velocity ata is not only reliably measure by unit C, but also properly transmitte to the other wireless controllers in real-time. The time histories of the inter-story rifts from the same centralize wireless control test are plotte in Fig. 4(b), together with the rifts of a centralize wire control test an a ynamic test when the structure is not instrumente with any control system. The same groun excitation is use for all the three cases shown in Fig. 4(b). The results show that both the wireless an wire control systems achieve consierable gain in limiting inter-story rifts. Running at a much shorter sampling time step, the wire centralize control system achieves slightly better control performance than the wireless centralize system in terms of mitigating inter-story rifts. To further compare ifferent ecentralize schemes with ifferent communication latencies, Fig. 5 illustrates the peak inter-story rifts an floor accelerations for the original uncontrolle structure an the structure controlle by the four ifferent wireless an wire control schemes, as efine in Table. 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 greatest reuction in the peak rifts an the 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 uniforme peak inter-story rifts an the smallest peak floor accelerations. This illustrates that in the ecentralize wireless control cases, the higher sampling rate (from lower communication latency) can potentially compensate for the limite ata when the sensor ata from faraway floors are ignore CONCLUSIONS This paper investigates the feasibility an effectiveness of ecentralize wireless control strategies in civil structures. We first introuce the theoretical backgroun for an optimal output feeback structural control system centralize an ecentralize communication patterns. Both numerical simulations an experimental tests are then performe to examine the traeoff between the egree of centralization an communication latencies. The simulate an experimental results show that ecentralize wireless control strategies may provie equivalent or even superior control performance, given that their centralize counterparts suffer longer sampling steps ue to wireless communication latencies. Laboratory experiments also successfully valiate the reliability of the prototype wireless 3 Maximum Inter-story Drifts No Control Wireless # Wireless # Wireless #3 Wire 3 Maximum Absolute Accelerations No Control Wireless # Wireless # Wireless #3 Wire y S tor F l o or Drift ) (a) Acceleration /s ) Figure 5. 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. (b)

10 structural sensing an control system. ACKNOWLEDGMENTS This research is partially fune by the National Science Founation uner grants CMS (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--3. 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. REFERENCES Celebi, M. (). 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, (5). 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 Engineering & Structural Dynamics, 4(5), Lin, P.-Y., P.N. Roschke, an C.-H. Loh (5). System Ientification an Real Application of a Smart Magneto-Rheological Damper, Proc. of the 5 International Symposium on Intelligent Control, Limassol, Cyprus, June 7-9, 5. Lynch, J.P. an K.H. Law (). Decentralize Control Techniques for Large-scale Civil Structural Systems, Proc. of the th International Moal Analysis Conf., Los Angeles, CA, USA, February 4-7,. Lynch, J.P. an K. Loh (5). A Summary Review of Wireless Sensors an Sensor Networks for Structural Health Monitoring, Shock an Vibration Digest, 38(), 9-8. Lunze, J. (99). Feeback Control of Large-scale Systems, Prentice Hall, Hertforshire, UK. MaxStream, Inc. (5). XStream OEM RF Moule Prouct Manual, Linon, UT, USA. Ploplys, N.J., P.A. Kawka, an A.G. Alleyne (4). Close-loop Control over Wireless Networks, IEEE Control Systems Magazine, 4(3), Solomon, I., J. Cunnane, an P. Stevenson (). Large-scale Structural Monitoring Systems, Proc. of SPIE Non-estructive Evaluation of Highways, Utilities, an Pipelines IV, Newport Beach, CA, March 7-9,. Soong, T.T. an B.F. Spencer, Jr. (). Supplemental Energy Dissipation: State-of-the-art an State-of-thepractice, Engineering Structures, 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., A. Swartz, J.P. Lynch, K.H. Law, K.-C. Lu, an C.-H. Loh (6a). Wireless Feeback Structural Control with Embee Computing, Proc. of the SPIE th International Symposium on Nonestructive Evaluation for Health Monitoring an Diagnostics, San Diego, CA, USA, February 6 - March, 6. Wang, Y., J.P. Lynch, an K.H. Law (6b). A Wireless Structural Health Monitoring System with Multithreae Sensing Devices: Design an Valiation, Structure an Infrastructure Engineering, in press. Wang, Y., R.A. Swartz, J.P. Lynch, K.H. Law, K.-C. Lu, an C.-H Loh (6c). 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, 6.