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4th International Conference on Earthquake Engineering Taipei, Taiwan
October 12-13, 2006
Paper No. 201 DISTRIBUTED SENSOR APPLICATIONS FOR CIVIL INFRASTRUCTURE
MONITORING AND SENSING RICH APPROACH TO THE DESIGN OF MODERN MECHATRONIC SYSTEMS
Gregory L. Fenves1 and Masayoshi Tomizuka2
ABSTRACT
Sensor technology is having a dramatic effect on improving the performance for a wide scale of engineering systems. In the first part of the paper advances in sensors sensor networks on large-scale civil infrastructure are examined. The civil infrastructure includes constructed facilities such as for transportation, water resources, and the built environment, particularly in densely populated urban regions. A wireless sensor network with time synchronization and reliable data transfer is developed and applied to structural health monitoring of bridges. The second part of this paper explores advantages of aggressive usage of sensors in the design and operation of mechatronic systems, i.e. sensing rich approach. Two examples of such approaches are the power transmission mechanism or drive train under servo control and the fault diagnosis and identification for vehicles for automated driving. Aspects of distributed sensor systems for civil infrastructure and mechatronic systems will be identified and suggestions made for future research that will leverage new developments for a wide range of engineering applications Keywords: Civil infrastructures, Sensors, Structural Health Monitoring, Mechatronic Systems
INTRODUCTION Sensor technology is having a dramatic effect on improving the performance for a wide scale of engineering systems. The first part of this paper examines advances in sensors sensor networks on large-scale civil infrastructure. The civil infrastructure includes the constructed facilities such as for transportation, water resources, and the built environment, particularly in densely populated urban regions. Traditional civil infrastructure is designed to be passive under long-term use. New sensor technology is providing the ability to monitor, diagnose, and make decisions about the condition of infrastructure, the prognosis of future performance, and inform decision-making. Examples of sensors and sensor networks for monitoring infrastructure performance under operating conditions and for extreme events are discussed, and a deployment of a wireless network with time synchronization and reliable data transfer is described. Trends in sensor technology and the architecture of sensor networks as distributed computing systems will provide new opportunities for improving the performance and reducing the life-cycle cost of civil infrastructure. The second part of this paper explores advantages of aggressive usage of sensors in the design and operation of mechatronic systems, i.e. sensing rich approach. Advantages include enhanced system 1 T.Y. and Margaret Lin Professor of Engineering, Department of Civil and Environmental Engineering, University of California, Berkeley, [email protected] 2 Cheryl and John Neerhout, Jr. Distinguished Professor of Engineering, Department of Mechanical Engineering, University of California, Berkeley, [email protected]
performance, added functionality, flexible operation of the system and reduced design and operational costs. Such advantages of sensing rich approach will be elaborated by two examples. The first example is the power transmission mechanism or drive train under servo control, which represents a fundamental element of any motion control system. The sensing rich design is based on distributed measurements of positions and accelerations at multiple points of a drive train. These measurements improve the performance of drive train servo systems in terms of speed, positioning accuracy and vibration suppression. The second example is vehicles for automated driving. The California PATH (Partners for Advanced Transits and Highway) Program invested a significant amount of effort on various aspects of automated driving on automated highways. This effort has shown the technical feasibility of automated highway systems based on a number of sensors. DISTRIBUTED SENSOR APPLICATIONS FOR CIVIL INFRASTRUCTURE MONITORING Large-scale civil infrastructure systems include long-span bridges, highway and transportation networks, water storage and transmission systems, power generating plants and transmission systems, and port, harbor, and inland waterway systems. Large and important buildings can be considered part of the infrastructure because of services they provide. These complex systems are subject to ongoing deterioration and degradation from use and environmental influences, as well as damage or failure caused by natural hazards and man-made intentional hazards. Distributed sensor networks that provide information about condition and operation have tremendous promise for improving the functionality, safety, and security of infrastructure systems. Recent advances in MicroElectro-Mechanical-Systems (MEMS) have created a new generation of small, inexpensive and low-power devices that can sense physical response quantities (acceleration, displacement, strain, temperature, light, sound, etc.), perform analog and digital filtering and local computation on the signal, and wirelessly communicate it. Advances in wireless communication, networking, and data management have made it possible to deploy networks of hundreds of these smart sensors for a range of applications. Wireless sensor network applications to-date have required low duty cycle and low power consumption. Environmental and habitat monitoring are leading applications, with ZebraNet (Juang, et al. 2002), James Reserve (Hamilton and Rotenberry, 2006), a vineyard in British Columbia (Yarvis, et al. 2002) and Great Duck Island (Mainwaring, et al. 2002) as examples. Infrastructure system sensor networks can be considered as a multi-level stack illustrated in Fig. 1. Sensors are combined with local processing such as filtering, feature identification, and event detection. One of the most important aspects of scalable sensor networks is aggregating the temporal and spatial features of the data. Since aggregation, such as correlation between the signals from multiple sensors, involves communication, and wireless communication is power intensive, there is an important need for compression methods that are applicable to infrastructure systems. The next stage is identification of properties and state of the system using the aggregated data. Identification allows diagnosis of a system, whereas the next stage, model updating, uses the identified information for forward simulation of prognosis. Finally, diagnosis (current state) and prognosis (expected state) provide the knowledge needed for decision-making about operations, intervention, safety, and repair of the infrastructure system. Wireless network sensor systems have great promise for infrastructure systems, particularly major structures such as long-span bridges and tall buildings. Instrumentation of structural systems has typically been for scientific purposes, but the goal of the ongoing research is to use sensor networks for providing continuous information about the state of structural health. Consider an important bridge instrumented by hundreds of smart wireless sensor motes at and near critical points in structure that report information necessary to evaluate the structural health. The network can also be used to diagnose structural problems and provide a prognosis about the effects of the changing condition. To support these higher-level applications it is necessary to increase dramatically the spatial density of sensors in building, and develop algorithms to process the large amount of data on the network and translate it to a state of health of structure.
Figure 1. Sensor network stack. In the civil structural area, Spencer et al. (2004) provided an overview of opportunities and challenges for sensor networks based on integration of sensing and signal condition within the sensor device and wireless network communication. Chung et al. (2004) investigated a wireless sensor network for a short bridge. Lynch et al. (2004a,b) investigated damage detection algorithms for wireless sensor networks. Masri et al. (2004) developed a web-based wired sensor network for a long-span suspension bridge and investigated system identification methods. Ko and Ni (2005) present developments in structural health monitoring for long-span bridges, including a wired sensor network on the longest suspension bridge in the world with accelerometers, GPS, and fiber optic strain sensors. As discussed in regards to Fig. 1, compression is very important for efficient use of wireless communication. For structural applications, Zhang and Li (2006) used dynamic properties identified by auto-regressive methods to compress data. Lei et al. (2005) developed algorithms for time synchronization of wireless networks for structural health monitoring. Xu et al. (2004) developed a wireless sensor network for structural monitoring, incorporating many of the important features of reliable data transport, time synchronization, and compression. Civil Structure Sensor Network Application Civil structural applications require aggressive robust wireless sensor networks with high-fidelity sampling, collection of large volume of data and more sophisticated capability of on-board signal processing. To address these issues, a specific-purpose program was created in Intel-Berkeley Lab, which allows for a great deal of robustness and efficiency for structural health monitoring applications. Software components have been developed to support high-frequency sampling, reliable data transmission, and efficient control of services on the sensor board. The remainder of the first section of this paper describes the design and implementation of a wireless sensor network for structural health monitoring that has been developed at UC Berkeley as a result of collaboration between civil engineers, electrical engineers, and computer scientists (Pakzad and Fenves, 2004; Pakzad, et al. 2005). The focus of the current development is on the sensing/local processing and spatio-temporal aggregation of the network stack (Fig. 1). The other levels in the stack are discussed. This work has been sponsored by the National Science Foundation through UC Berkeley’s Center for Information Technology Research in the Interest of Society3. The design goal for the sensor network application is the structural health monitoring of a large civil structure, such as a long-span bridge, at a highly seismic earthquake site. There are three primary objectives for the network: 1) monitor global and local vibration properties of the bridge under ambient vibration, 2) estimate displacements of the main span under heavy wind loads, and 3) assess structural response in an earthquake. The sensor network needed to be scalable to a large number of
3 NSF Award No. CISE/ITR 0122599 to UC Berkeley. The work described in this section represents research by Professors D. Culler and J. Demmel, graduate students S. Pakzad and S. Kim, and the first author.
sensors to provide adequate spatial resolution. Furthermore, the wide range of motion to be monitored from ambient vibration to earthquake response presented a challenge. To meet these objectives using available MEMs devices, accelerations were selected as the physical quantity to be measured by the network. Accelerations are easy to measure and can be used for extracting vibration properties of the structure. It is recognized that lower vibration modes of a structure are not sensitive to changes in structural properties from damage or degradation, but one of the research questions is how to estimate local changes using dense spatial arrays of sensors. Accelerations with low noise floor can be used to estimate displacements, although as accurate GPS enabled MEMs become more available, data fusion from accelerometers and GPS can be done in the future. Finally, acceleration is the most convenient for assessing structural response in an earthquake. Hardware Design For structural health monitoring applications using accelerometers, we have developed a mote that consists of a control and communication device and a sensor board. The mote originally used the MICA2 mote from Crossbow Technology, which was later upgraded to MICAz. The microprocessor has a Central Processing Unit (CPU), flash memory, EEPROM and RF tunable frequency radio. (see http://www.tinyos.net/scoop/special/hardware). The MICA Sensorboard is the default sensor board for this class of motes, and it has sensors for temperature, photo, accelerometer, magnetometer, microphone and sounder (see http://webs.cs.berkeley.edu/tos/hardware/sensorboard.html). Ruiz-Sandoval et al. (2006) also used similar Berkeley-based hardware for structural health monitoring applications. This standard sensor board, however, was not sensitive enough for structural health monitoring applications because the accelerometer did not have sufficient sensitivity for ambient vibrations and it only has a 10-bit A/D converter. Therefore, an accelerometer sensor node has been designed that meets the three objectives described earlier for structural health monitoring. The node is designed to operate in a wireless sensor network using low-power radio communication to provide highly scalable deployment. A schematic of the sensor node is shown in Fig. 2. The following subsections describe the major hardware components. Figure 2. Schematic diagram of sensor node. Sensors Two types of accelerometers are used for the sensor node. The high-range accelerometers are ±2g to measure earthquake response of the structure. The low-range accelerometers have an accuracy on the order 10’s of µg for measuring ambient vibration and estimating wind displacements by double integration. A node has one of each type of accelerometer in a horizontal direction and the vertical direction, for a total of four channels. For the high-range, the industry-standard ADXL202 by Analog Devices is used. It is a low-cost, low-power complete two-axis accelerometer that measures both dynamic acceleration (vibration) and static acceleration (gravity). Its physical size is 5 mm x 5 mm x 2 mm. The nominal resolution is 200 µg per root-square Hz, allowing signals below 2mg (at 60 Hz bandwidth) to be resolved. For the low-
range, SiliconDesign1221 provided a low-noise (2µg/(root Hz)) and relatively low-cost single-axis accelerometer, which responds to both static and dynamic acceleration and provides the sensitivity required for ambient vibration analysis. Both Chung et al. (2004) and Spencer et al. (2004) selected similar accelerometers in their investigations, but technical issues on calibration, time synchronization, and reliable wireless communication were not addressed. The ADXL202 has a built-in anti-aliasing low-pass filter with a cutoff frequency of 25 Hz. The analog outputs of SiliconDesign1221 sensors are passed through a single-pole (-6db) low-pass filter with a cutoff frequency of 25 Hz as well. The range of SiliconDesign accelerometer is reduced to ±0.1g so that using a 16-bit A/D converter, and a nominal resolution of 3µg is achieved. Our tests showed that the noise floor of the accelerometer itself is controlling and the device is sensitive to within 30µg, as discussed subsequently. Each node is also equipped with a thermometer and the data are stamped, at a lower sampling frequency, with the temperature. This is an important piece of information since our tests showed that the sensors are sensitive to temperature changes and the temperature information is necessary for calibration of the accelerometers. Data Acquisition and Processing As mentioned in the introduction of this section, the microprocessor is MICA2 mote, which was later upgraded to the MICAz. Data acquisition consists of sampling the signals from the sensors and having measures to characterize the fidelity of the data and its noise level. The sensor node is designed for high-frequency sampling, up to 1000 Hz, to allow for oversampling of structural response for which 100 Hz is ample. The oversampling provides the opportunity for simple analog filtering and significant noise reduction, which is particularly important for the low-level ambient accelerations. The analog processing simply consists of single-pole low pass filter on each of the four channels for anti-aliasing. A 16-bit Analog-to-Digital converter digitizes the signal. The digital processing performs averaging to decimate the signal to 100 Hz. For the high sampling rates, a jitter control feedback loop is critical for reducing signal distortion by inaccurate time synchronization of nodes. Fig. 3 shows the sensor board with the one ADXL202 and two SiliconDesign1221 MEMs accelerometers on it. (The MICA mote is not plugged in so that the board can be seen.) Software Design The MICA motes have the operating system TinyOS, which is an open-source operating system designed for wireless embedded sensor networks (Levis, et al. 2004). The software architecture of TinyOS enables rapid implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyDB is an example of a general-purpose program, based on TinyOS, which is used for many applications (see http://telegraph.cs.berkeley.edu/tinydb/). The standard TinyOS lacks features needed for structural health monitoring applications. The two most important of those are high frequency sampling and reliable data transfer. The next two subsections explain the problem and how they were dealt with using capabilities of TinyOS. High-Frequency Sampling Most structural systems have fundamental frequencies of much less than 20 Hz. Tall buildings generally have a fundamental frequency of less than a 1Hz; long-span bridges and other major structures have fundamental frequencies of even lower than that. Rarely a structure is stiff enough to even approach a fundamental frequency of around 10 Hz. High frequency sampling, however, is desirable for two reasons. For local damage detection is necessary to have accurate data to identify higher modes of vibration, perhaps up to 10’s of Hz. Secondly, high sampling rates lower the power
of the noise through oversampling. Consequently, the software is designed to control sampling rates of up to 1000 Hz level and the processing downsamples by averaging to between 100 Hz and 200 Hz depending on the application.
Figure 3. Sensor board at approximately full size. The three MEMS accelerometer are on the left for
measuring four channels of acceleration transverse to the board and vertical. The four low-pass filter circuits and A/D converters are in the middle left, and the connector for the MICA mote is middle-right (the mote is not shown).
Also important in structural monitoring is the uniformity of sampling intervals, thus jitter control becomes a significant problem at high sampling rates. There are two sources of jitter, temporal jitter and spatial jitter. Temporal jitter happens within a node when the actual sampling intervals deviate from their nominal lengths. Spatial jitter is a result of variation in hardware of different nodes and imperfect time-synchronization. Controlling temporal jitter has been addressed in the current version of the code, while dealing with spatial jitter is left to future work. Existing TinyOS components would only allow sampling of up to 200 Hz. To address this limitation, a new component in TinyOS, called HighFrequencySampling, has been developed in Intel-Berkeley lab that enables KHz range of sampling. This new component uses direct access to hardware timer (MicroTimer) and a new flash-memory writer with two separately controlled buffers (BufferLog). With HighFrequencySampling a sampling rate of up to 6.67 KHz was achieved (Kim, et al. 2004). Large-Scale Data Transfer High frequency sampling produces large volume of data, which cannot be transmitted in real-time through the limited bandwidth of wireless radio. Data is stored in the flash memory and is transmitted once the data-collection phase is over. To analyze the data transfer problem for a sensor network with high-frequency sampling, consider a network of 100 nodes, each with 4 channels of data with sampling rate of 200 Hz. Each sample consists of 4 Bytes of data and 4 Bytes for time-stamp. That means each board produces 384 Kbytes of data per minute, and the network produces 38.4 Mbytes of data per minute. With a rate of 1.2 Kbytes/Sec radio bandwidth, it takes about 9 hours to download a minute worth of data off the network. That makes the bandwidth even more precious, and hence a method to assure reliability of
data transfer is needed. To address this problem, a method was implemented for the reliable transfer of large amount of data (Kim, et al. 2004). Large-scaleReliableTransfer (LRX) is a new component created within TinyOS to provide reliable transfer of data from one mote to another and eventually to a server. LRX assumes that data resides in RAM. Each data cluster has a data description and is composed of several blocks, and each block fits into one packet. After looking at this data description, the receiver may accept or deny data (receiver already has that data, or that data is no longer useful, etc.). Explicit open-handshake is used in the transfer. The protocol can be described as selective acknowledgement and retransmission. Data transfer is composed of multiple rounds. In each round sender sends packets of missing data of the previous round. At the end of each round the receiver sends acknowledgement, saying which packets are missing. The sender then, sends the missing packets again. Sensor Network Testing and Calibration A variety of tests were performed to determine the noise characteristics of the sensor board. They included tests in a quiet environment to estimate the noise floor, as well as shaking table tests to investigate the performance of the board in a dynamic range and at higher frequencies. This investigation was essential in assuring the reliability and accuracy of the measured data for a large-scale civil structure deployment. The quiet environment test was first performed at the Berkeley Seismological Lab (McCone Lab) and then repeated in an isolated vault (LBL Vault). Only the high-resolution sensor of SiliconDesign was used in those tests. The calibrated data as well as the lower frequency section of the absolute value of Fourier Transforms of both tests are plotted in Fig. 4(a). The spectra confirm that the LBL Vault environment is much quieter than McCone Lab. The accelerometer was oriented in the horizontal direction in both tests; hence the peak at around 8 Hz in the spectrum of the test at McCone Lab could be contributed to a transverse vibration mode of the building, which has a rigid reinforced concrete structure with shear walls. The test at the LBL Vault showed a slow drift in data, which is due to temperature changes and confirms that the sensors need to be calibrated for temperature. A high-accuracy piezoelectric sensor was available at the LBL Vault, and the power spectral density plots of all the three signals are presented in Fig. 4(b). Since the low-frequency noise level is –84db, assuming a bandwidth of 25 Hz, the equivalent RMS acceleration noise is 32µg. Dynamic range testing was also conducted using a low-noise mass-spring vertical shaking table. The shaking frequencies were 0.5 Hz, 1 Hz, 2 Hz, 4 Hz and a sweep from 4 Hz to 8 Hz, in which approximately 30 sec worth of data was collected at each frequency for a total of 150 sec. Both of SiliconDesign1221 and ADXL202 accelerometers in the vertical direction collected data during the test. A piezoelectric accelerometer with sensitivity of the order of nano-g was used as reference. Fig. 5(a) shows part of the time-history plot of the dynamic test for both SiliconDesign and ADXL sensors. The lower-sensitivity ADXL accelerometer very closely matches the high-resolution SiliconDesign accelerometer, but naturally with a higher noise level. Figure 5(b) is the spectrum of the signals and confirms the agreement match between the two devices. Figure 5(c) is an averaged PSD plot of the two signals that confirms higher sensitivity of SiliconDesign devices compared to ADXL in the range of frequencies of interest. The data also show that the ADXL device has a significantly higher low-frequency noise than SiliconDesign device. After the extensive noise testing, the sensors were individually calibrated for both acceleration and temperature measurements. The calibration process consisted of a series of tilt tests in a range of temperatures. The board was rotated a few degrees at a time, using a high-accuracy tilting machine and the output of the A/D converter of the board at each station was recorded. The tests provided calibration information and confirmed that the accelerometers are very linear within their range.
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(a) (b) Figure 4. Noise level testing of SiliconDesign1221 sensor: (a) Time and frequency plots of quiet test
in McCone Lab and LBL Vault and (b) noise power spectral density of sensor at LBL Vault and McCone Lab and reference low-noise piezoelectric accelerometer.
Application to Structural Health Monitoring The wireless sensor network was first tested on a modern, arch bridge used for pedestrians, which is shown in Fig. 6(a). The sensor network was deployed with 14 sensor nodes and one base station as illustrated in Fig. 6(b). The acceleration data were collected at the base station and used in an ARX algorithm to identify the vibration mode properties. The first and second vertical modes from the identification are illustrated in the remainder of Fig. 6.
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Figure 5. Selected data from shaking table testing and noise characterization of MEMs accelerometer sensors (Part c continued on next page).
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(a) Tied-Arch Pedestrian Bridge
(b) Network deployment with 14 sensor nodes on
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(c) First vibration mode.
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Figure 6. Test deployment of wireless sensor network and modal identification of lower vibration modes using ARX methods.
Bddddd
Other SystemsThe Internet,
wireless communication
Human-Machine Interface
human factors
Computer/DSP
decision making
Sensorsenergy conversionsignal conditioning
Actuation
power modulation
Physical System
mechanical, electrical, etc. and their combinations
SENSING RICH APPROACH TO THE DESIGN OF MODERN MECHATRONIC SYSTEMS4 Mechatronic Systems Mechatronics is the synergistic integration of mechanical engineering with electronic and intelligent computer control in the design and manufacture of industrial products and processes. Figure 7 shows a modern mechatronic system. As shown in the figure, the physical system and the computer (DSP) are interfaced by sensors and actuators; in fact, modern mechatronic systems may not exist without sensors. Innovations and progress in sensor technologies have stimulated the advancement and development of control theories and technologies. Figure 7. Modern mechatronic system. Modern signal processing and estimation theories allow us to extract the maximum amount of information from each sensor signal. This allows the designer to minimize the cost for sensing, which may be an important consideration in the design of consumer products. On the other hand, the minimization of sensing cost may be only a secondary issue if the designer’s concern is to ensure high reliability and enhanced safety. In such applications, even redundant sensors for a critical signal may be a wise approach. It is also possible to design simple and robust control algorithms by fusing different kinds of sensors. In this paper, we examine advantages obtained by exploiting sensor signals in the control and monitoring of engineering systems. Sensing Rich Design of Drive Trains Drive trains refer to the mechanism that transmits the force/torque of actuator to the load or end-effector. In robots or assembly machines, the end-effector may be a gripper carrying a peg and inserting it to a hole. Drive trains are key elements in motion control systems. There are many variables in drive trains that can be measured. In conventional systems, sensors are installed on the motor side only, and it is typical that control is based on the current and position of the motor. In motion control systems, a key variable from the viewpoint of control is velocity. The velocity feedback control introduces damping to stabilize the system. Mechanical friction is a major disturbance source, and the friction force is a nonlinear function of the velocity. It is possible to estimate the velocity based on current and position measurements by a model based Kalman filter, but 4 Work in this section was in part supported by National Science Foundation (Award #CMS-0529451), FANUC, Ltd. and the California Department of Transportation.
the estimation error significantly depends on model parameters such as mass and mechanical damping coefficient. To overcome this problem, it has been suggested to fuse the acceleration and position measurements by the kinematic Kalman filter (Lee and Tomizuka, 2001; Jeon and Tomizuka, 2004). Acceleration and position are closely related, and one may argue whether it is necessary to measure both of them. In fact, it is not necessary in mathematical sense, but it is advantageous from a practical point of view. For example, the kinematic Kalman filter does not depend on model parameters, and it provides an accurate estimate of the velocity. Kinematic Kalman Filter The kinematic Kalman filter is explained for a single degree of freedom motion system described by
m!!x + b !x + kx = u (1) where x is the position, u is the force input, and m, b and k respectively denote mass, damping coefficient and spring constant. The upper dot on x denotes time differentiation. When the position is measured, the velocity may be estimated by a model based Kalman filter or state observer. Defining two state variables by x1 = x and x2 = dx/dt, the system and the Kalman filter are obtained in the following form. System:
d
dt
x1(t)
x2(t)
!
"##
$
%&&=
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"#
$
%&
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x2(t)
!
"##
$
%&&+
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1/ m
!
"#
$
%&[u(t)+ w(t)]
y(t) = x1(t)+ v(t)
where w(t) and v(t) are input noise and measurement noise, respectively. w(t) and v(t) are independent Gaussian white noise processes. Model Based Kalman Filter:
d
dt
x1(t)
x2(t)
!
"##
$
%&&=
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'k / m 'b / m
!
"#
$
%&
x1(t)
x2(t)
!
"##
$
%&&+
0
1/ m
!
"#
$
%&u(t)
+f1
f2
!
"##
$
%&&
y(t)' x1(t)!" $%
where f1 and f2 are the filter gains, which depend on systems matrices and noise characteristics. Note that the model based Kalman filter depends on the plant parameters. Thus, if the plant parameters are poorly known or subject to change, the performance may deteriorate: i.e. the performance of the model based Kalman filter is not robust. If the position and acceleration are both measured, we note that two quantities are related by double integrators. Thus the system equation and the KKF are obtained in the following form. System:
d
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x2(t)
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"#$
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y(t) = x1(t)+ v(t)
where wa(t) and v(t) are accelerometer measurement noise and position measurement noise, respectively.
Kinematic Kalman Filter:
d
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x2(t)
!
"##
$
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%&
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"#$
%& a
M(t)+
fk1
fk 2
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"##
$
%&&
y(t)' x1(t)!" $%
where fk1 and fk2 are the filter gains. Note that KKF does not depend on model parameters at all. Thus, if the acceleration and the position are both measured, the velocity may be reliably estimated by KKF. Since the encoder measurement is obtained only at sampling instances, it is more natural to develop KKF in the discrete time domain. In a recent paper, Jeon and Tomizuka (2004) investigated the use of an accelerometer and encoder along with the idea of KKFs for velocity estimation and control of a motor. As shown in Fig. 8, the KKF provides an accurate estimate of the velocity even when the resolution of an encoder is low.
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Figure 8. Estimation and control of speed by Kinematic Kalman Filter and Model Based Kalman
Filter (Encoder resolution: 256 pulses/rev). Accurate estimation of velocity by KKF brings a number of advantages to the design of servo systems (Tomizuka, 2006). Such advantages are reviewed in the following. Robust Compensation of Nonlinear Friction Friction force is nonlinear. If we add a nonlinear friction term to Eq. (1), the system equation becomes
m!!x + b !x + f ( !x)+ kx = u
If we have an accurate estimate of the velocity, the nonlinear friction term may be reliably cancelled by letting the control input be
u(t) = u '(t)+ f ( !x)
where f
is the estimate of the nonlinear friction force.
Easy Implementation of State Feedback Control One of the most fundamental discoveries in the state space control theory is the way that state feedback control alters the dynamics of the open loop system (e.g. Anderson and Moore, 1990). For example, given a linear controllable system, the closed loop eigenvalues may be arbitrarily assigned via state feedback control. If the state feedback control gain is determined based on the linear quadratic regulator (LQR) theory, the closed loop system exhibits excellent robustness. At an early stage of development of the state space control theory, researchers noted that it was not trivial to have direct access to the entire state variables for state feedback control. This motivated the model based state estimation theory and the estimator state feedback control theory. While estimator feedback control systems, such as linear quadratic Gaussian (LQG) control systems, have nice analytical properties such as the separation theorem, they do not possess the nice robustness properties of LQR systems. In the control of drive trains, the kinematic Kalman filter may be applied to each of inertia elements to obtain an accurate and robust estimate of the velocity. This normally allows the implementation of state feedback control such as LQR without model based Kalman filters.
Other advantages of accurate velocity estimation include an easy real time identification of model parameters such as mass and damping coefficient. Fault Diagnostic Systems for Automated Vehicles California PATH (Partners for Advanced Transit and Highways) conducted an extensive study on Automated Highway Systems (AHS) from the late 1980s to the early 2000s. This study was aimed to reduce congestion on highways by achieving significantly higher traffic flow through closer packing of automatically controlled vehicles into platoons. Analytical and experimental studies of automatic control of the longitudinal and lateral motion of cars have been undertaken to establish feasibility of AHS concepts (Hedrick, Tomizuka and Varaiya, 1994). The reliability and safe operation is critically important in AHSs. This issue was addressed by Rajamani, et al. (2001). They developed an automated health monitoring system for all the sensors and actuators used by the lateral and longitudinal controllers. The system uses parity equations and several reduced-order observers constructed from simplified dynamic models for longitudinal and lateral dynamics of the vehicle. The relationships among the sensor signals, the actuation inputs and the observer states were exploited to monitor and detect any faulty conditions of sensors and actuators. The effectiveness of the approach was verified by both simulation and experimentation. In the remainder of this section, the lateral control portion of the work of Rajamani, et al. (2001) is reviewed. Figures 9 and 10 show a PATH test vehicle and the PATH lateral guidance scheme for automated driving, respectively.
Figure 9. PATH test vehicle. Figure 10. PATH lateral control scheme.
The lateral position of the vehicle relative to magnets buried along the centerline of the road is measured by magnetometers on board the vehicle.
Vehicle Model (Lateral Dynamics) Dynamic models are of fundamental importance in the design of control and monitoring algorithms. A simplified lateral dynamics model incorporating only the lateral translation and yaw degrees of freedom is used for controller design and is available (Peng and Tomizuka, 1993). The simplified lateral dynamics model is derived by linearizing vehicle lateral dynamics with respect to the road centerline reference co-ordinates. It is:
!x = Ax + B
1! + B
2!"
d (2)
with x = y
cg!y
cg!" ! !"#
$%&
T
,
A =
0 1 0 0
0 a22
a23
a24
0 0 0 1
0 a42
a43
a44
!
"
#####
$
%
&&&&&
a
22=!2
mv(C
" f+C
"r) ,
a
23=
2
m(C
! f+C
!r) ,
a
24=!2
mv(!
fC
" f+ !
rC
"r) ,
a
42=!2
vI(!
fC
" f! !
rC
"r) ,
a
43=
2
I(!
fC
! f" !
rC
!r) ,
a
44=!2
vI(!
f
2C" f
+ !r
2C"r
)
B
1= 0 2C
! f/ m 0 2!
fC
! f/ I"
#$%
T
B
2= 0 b
220 b
24!" #$
T
b
22=!2
mv(!
fC
" f! !
rC
"r) ,
b
24=!2
vI(!
f
2C" f
+ !r
2C"r
)
where ycg is the lateral displacement of the c.g. of the vehicle with respect to road co-ordinates and !" is the relative yaw angle of the vehicle with respect to road co-ordinates. The front wheel steering angle ! is the control input to regulate lateral and yaw motion of the automated vehicle. Note also that the road reference co-ordinates rotate on curves. This effect is represented as the desired yaw rate !!d in Eq. (2). The other variables and symbols used in the model are as follows:
m Vehicle mass I Vehicle moment of inertia about c.g. v Vehicle speed
rf CC!!
, Front and rear cornering stiffness, respectively
rf ll , Distances from c.g. to front tire and rear tire, respectively
In implementing the lateral control system, vehicle lateral displacement is measured by a magnetic sensor system which measures displacement by measuring the magnetic field from discrete magnets buried every 1.2 meters in the center of the road. Magnetometers mounted on the car serve as sensors to measure the magnetic field. The output equation for lateral displacement is y C x1 1= with
C
1= [1 0 d
s0] (3)
where d
s is the longitudinal distance between the magnetometer and the vehicle c.g. In addition, on-
board inertial sensors such as a yaw-rate sensor and a lateral accelerometer are available and are typically used by the lateral control system. The outputs equations for these sensors are given below: Yaw-rate sensor:
y
2= C
2x with
C
2= [0 0 0 1] (4)
and lateral accelerometer:
y
3= C
3x + b
12! + b
22!"
dwith
C
3= [0 a
22a
23a
24] (5)
The lateral control systems developed and experimentally implemented at PATH include the frequency shaped linear quadratic control with preview (Peng and Tomizuka, 1993), linear controller using “virtual look-ahead” by using front and rear magnetometers (Tan, et al. 1998) and nonlinear sliding control (Hingwe and Tomizuka, 1997). Sensors and Actuators A review of the vehicle dynamics model and the controllers shows that the following sensors are needed by the lateral control systems: steering angle sensor, yaw rate sensor, magnetometer, lateral accelerometer, and steering actuator. Although redundant, a front wheel angle sensor may be added. Observer Design and Analytical Redundancy If three sensor signals are algebraically related so that there exist three independent parity equations relating these signals, then the three residues obtained from these parity equations can be used to determine exactly which of the three sensors is at fault (assuming that not more than one sensor becomes faulty at the same time). In the case of the lateral control system, the three sensors - yaw-rate sensor, lateral accelerometer and magnetometer - are used to design three different observers. Each observer uses two of these three sensor measurements and estimates the third sensor signal. This can be used to uniquely identify a fault in any one of the three sensors. In addition, steering wheel angle, vehicle wheel angle and commanded steering angle are related by parity equations and used to ensure that the steering actuator and the two angle sensors are working. Observer to Estimate Yaw-Rate: If we assume that the lateral acceleration and magnetometer sensors are working, the yaw-rate of the car can be estimated by an observer using these two sensors. The observability matrix
C
1
TC
3
T (C1A)T (C
3A)T!
"#$ (6)
has rank 4 which makes the states completely observable. The residue
R
14= C
2x ! y
2 (7)
can then be used to determine if the yaw-rate sensor is faulty.
Observer to Estimate Lateral Acceleration: If we assume that the yaw-rate and magnetometer sensors are working and that the steering angle sensor is not faulty, the lateral acceleration of the car can be estimated by an observer using these three sensors. The observability matrix
C
1
TC
2
T (C1A)T (C
2A)T!
"#$ (8)
has rank 4 which makes the states completely observable. The residue
R
15= C
3x ! y
3 (9)
can then be used to determine if the lateral acceleration sensor is faulty. Observer to Estimate Lateral Displacement: If we assume that the lateral acceleration and yaw-rate sensors are working, one could try and estimate lateral displacement, usually measured by the magnetometer. However, the observability matrix
C
2
TC
3
T (C2A)T (C
3A)T!
"#$ (10)
has a rank of only 3 which means the complete state is not observable with these measurements! The partial state-vector with dynamics defined by
d
dt
!ycg
!"
! !"
#
$%%
&%%
'
(%%
)%%
=
a22
a23
a24
0 0 1
a42
a43
a44
*
+
,,,
-
.
///
!ycg
!"
! !"
#
$%%
&%%
'
(%%
)%%
+
b22
b23
b24
*
+
,,,
-
.
///!"
d+
b12
b12
b14
*
+
,,,
-
.
///0
(11)
is, however, completely observable from these two outputs. This means that while
y
cg is not
observable, !y
cg can be estimated from the lateral accelerometer and yaw-rate sensor. If the initial
condition y
cg(0) is known, then
y
cgcan be estimated as follows:
y
cg(t) = ! !ycg
dt + ycg
(0) (12)
The residue
R
16= y
cg(t)! y
cg(t) (13)
can then be used to determine if the magnetometer sensor is faulty. Fault Diagnostics of Steering Angle Sensor / Steering Actuator If we assume that the steering wheel angle and the vehicle wheel angle are both measured, then the two sensors are related by a scaling factor. The steering wheel angle, vehicle wheel angle and commanded steering angle are related by three independent parity equations.
The following three residues are then calculated by using different combinations of the above three signals:
=11R commanded steering angle/ measured steering angle.
=12R commanded steering angle/ measured vehicle wheel angle.
=13R measured steering wheel angle/ measured vehicle wheel angle.
The truth table, Table 1, can then be used to detect a fault in any one of the following three components: steering actuator, steering angle sensor, vehicle wheel angle sensor. A System for Automated Fault Diagnostics So far, there have been generated 6 residuals. By processing them, it is possible to identify a fault in any of the sensors or steering actuator involved in the lateral control. Table 2 shows how a fault in any one of the sensors or steering actuator causes a unique combination of residues to grow. It should be noted that the fault diagnostic is not designed to handle simultaneous multiple sensor or actuator failures.
Table 1. Truth Table for Steering Angle / Steering Actuator Fault Detection
Faulty Component 11R
12R
13R
Steering actuator High High Low
Steering angle sensor High Low High
Wheel angle sensor Low High High
Table 2. Behaviour of Residue Under Sensor/Actuator Faults
CONCLUSION The next generation of wireless sensor networks will provide scalable systems to monitor and assess the civil infrastructure. A new sensor node platform has been developed with calibrated multi-range accelerometers within a wireless network using accurate time synchronization and reliable large-scale data communication over multiple hops. As a prototype project, a network of about a hundred of MEMS wireless motes will be deployed on Golden Gate Bridge to monitor the bridge for ambient vibrations (traffic, wind, etc.) and also to record the response of the bridge in the event of an earthquake. The motes will be placed on critical locations of both towers as well as the main span, and will form a wireless network that nearly continuously records data and transmit it to a central
Faulty Sensor/ Actuator 11R
12R
13R
14R
15R
16R
steering angle sensor H L H H H H magnetometer L L L H H H yaw-rate sensor L L L H L H lateral accelerometer L L L L H H steering actuator H H L L L L wheel angle sensor L H H L L L
server for further analysis. The future directions that will be enabled by the increasing computational power of sensor networks include not only sensing and spatio-temporal aggregation of data, such as for health monitoring, but integration with model updating using the sensor network as an embedded computing system. Sensing is of fundamental importance in the operation of modern engineering systems. Available measurements should be utilized to obtain maximum benefits and advantages. Modern signal processing and control methodologies are useful in this regard. In the system design, the goal is not necessarily to minimize the number of sensors. Sensing rich approaches or redundancy among sensing signals will bring various advantages such as robust control and health monitoring and diagnosis of the systems.
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