SENSOR FUSION FOR LINEAR MOTORS, AN APPROACH FOR LOW-COST MEASUREMENTS

Size: px
Start display at page:

Download "SENSOR FUSION FOR LINEAR MOTORS, AN APPROACH FOR LOW-COST MEASUREMENTS"

Transcription

1 Proc. of Mechatronics 22, University of Twente, June 22 SENSOR FUSION FOR LINEAR MOTORS, AN APPROACH FOR LOW-COST MEASUREMENTS Bas J. de Kruif, Bastiaan van Wermeskerken, Theo J. A. de Vries and Maarten J. Korsten University of Twente, Drebbel Institute for Mechatronics, P.O. Box 217, 75 AE Enschede, The Netherlands for information please Abstract A new measurement setup for a linear motor is investigated. It is custom to use an optical ruler as a measurement device for a linear motor. The high position measurement accuracy that is obtained with this ruler, is required to get an accurate estimate of the velocity to be used in the velocity feedback loop. The accuracy is not required for the position feedback loop. An optical ruler is an expensive measurement device and because the accuracy is higher than needed for the position measurement, it could be advantageous to use a cheaper sensory system to measure the position and to use auxiliary sensors to get a good estimate of the velocity. The permanent magnets that are inherently present in a synchronous PM motor can be used as a coding such that no external ruler is necessary. Hall sensors are used to measure the magnetic field of these magnets and determine the position based on this magnetic field. The velocity estimate derived from this measurement does not meet the specifications and therefore an acceleration sensor is added. The acceleration sensor in combination with the position sensors estimate the velocity with an accuracy that meets the specifications for a considerably smaller price than the optical ruler. 1 Introduction Linear motors are typically used in applications that require high speed, high force and high precision actuation. The high precision, i.e. position accuracy, can only be achieved if good measurements are present for the feedback. For fluent control a good estimation of the position is not enough and a good velocity estimate is also needed. It is common to use an optical ruler to measure the position (Gieras & Piech 2). The velocity is not actually measured but calculated from the position measurement. This velocity estimation uses differentiation and is therefore noise sensitive. High quality position measurements are needed to get satisfactory velocity estimates. Typically, the deciding factor in selecting a position sensor for a linear motor is the resolution that is

2 Proc. of Mechatronics 22, University of Twente, June 22 required in order to obtain a sufficiently good velocity estimation, and not the accuracy that is required for the position feedback loop. An optical ruler is an expensive measurement device and it would be economically attractive to replace it with a less costly substitute. The positional accuracy of the substitute may degenerate for the control of the motor, however, the quality of the velocity estimation may not decrease. The required accuracy, the distance between the measured quantity and the true quantity, is given to be 5 µm for the position estimate and should be accurate within 1.7 mm/s for the velocity estimate. In this paper, we propose such a measurement system. This paper is organized as follows. In section 2 the background of the method is treated. The concepts by which we try to substitute the optical ruler are given here including some theory. In section 3 the requirements placed on the hardware are discussed. With the new measurement setup a set of experiments is performed. These experiments are treated in section 4. Finally a conclusion is drawn in section 5. 2 Background Optical rulers can be used to obtain a good position measure and based on this measurement a velocity estimate can be deduced. An optical ruler consists of two parts: 1. a strip with some form of coding that can be read with optical means 2. a reader that measures position by reading the coding of the strip High accuracies are achievable with such systems. However, for medium accuracies, a ruler is relatively expensive, due to the fact that the cost price for an optical reader is high and because a specific strip with accurate coding always has to be added to the motion system. It is constructional unattractive to include a strip in the motion system. In order to substantially lower the costs, we should look for a measurement system based on much cheaper hardware. This may be realized by considering solutions that meet the following principles: 1. functional interaction: measure motion characteristics on basis of inherently present actuator properties instead of using a separately added measurement device. 2. sensor fusion: measure motion characteristics with multiple low cost devices instead of one costly device Function interaction: We wish to combine the inherently present actuator properties for the estimation of the position. The most obvious choice is to use the magnets that are present for the generation of a force as a coding. The magnetic field can be related to the position of the translator. Sensor fusion: We want to combine several magnetic field measurements to one position estimate with a higher accuracy than a single sensor can achieve. Secondly, we want to fuse this position estimate with an acceleration measurement to get a velocity estimate. All these combinations are done at a signal level and it is therefore called signal-level sensor fusion (Lou & Kay 1992). This two-stage fusion scheme is depicted in figure 1. In this figure the block SF measures the position from several magnetic field measurements and the block K estimates the states from the position and acceleration measurement.

3 Proc. of Mechatronics 22, University of Twente, June 22 X1 X2 X3 Xn SF xˆ a K xˆˆ ∠vˆˆ Figure 1: Sensor fusion scheme for estimation of the states 2.1 Position measurement The position is measured by a combination of functional interaction and sensor fusion. If only one Hall sensor is used to measure the magnetic field of the permanent magnets, the spatial sensitivity might be too low in certain regions, and therefore several magnetic field sensors can be used. The combination of these measurements is the sensor fusion part. The spatial sensitivity of one Hall sensor that can measure the complete range variation of the magnetic field might be too low to obtain the required positional accuracy as stated before. The spatial sensitivity is given as the derivative of the output of the Hall sensor relative to the position. The output of one sensor as function of the position is given in figure 2. The location of two magnets is included in this figure. The spatial sensitivity is zero at the top and the bottom of the sine because the tangent is horizontal. If an AD-card is used to read the output, the position range that corresponds to this digital value would be large compared with other positions. Should the Hall sensor be moved closer to the magnets, the output will saturate. Both unsaturated and saturated sensor outputs are given in figure 2. It can be seen that the spatial sensitivity is larger for the saturated sensor if it is not saturated. If we use multiple sensitive Hall sensors that saturate most of the time but that are located in such a way that there is always one out of saturation, we are able to increase the spatial sensitivity as much as desirable. This is illustrated in figure 3. In this figure it can be seen that if several sensors are used, the spatial sensitivity is nearly equal in the complete domain. The numbers in this figure denote the sensor number. The number of required Hall sensors for the given accuracy will be calculated in section 3. To obtain an estimate of a position based on the strength of the magnetic field, the magnetic field should be known as function of the position. If all the magnets are the same and their position relative to each other is equal, the position can be calculated by the strength of the magnetic field and the number of magnets that are passed. However, there is some tolerance on the magnet strength and their placement which is unknown. This tolerances don t need to introduce new inaccuracies. The output of the sensor can be stored in a table as function of the position during an identification phase. By comparing an output with the stored outputs and by including the number of passed magnets, the position can be determined. The tolerances on the spacing and the strength are incorporated in the table and shall only give small errors due to interpolation between measured values. 2.2 State estimate The set of Hall sensors gives a measurement of the position. This measurement can be combined with an acceleration measurement to get an estimate of the velocity. This

4 Proc. of Mechatronics 22, University of Twente, June 22 Hall output output ma Hall output output ma output min output min N S position Figure 2: The sensor output as a function of the position at different distances to the permanent magnets N S position Figure 3: The output of several sensors close to the permanent magnets and displaced relative to each other combination is depicted with the block K in figure 1. The combination of measurements to obtain non-measured, but dependent, variables can be done using a Kalman filter (Ljung 1999). To obtain an estimate of nonmeasured variables, the Kalman filter includes an internal model of the system. The measurements of the variables are compared with the states of this model and the states of the internal model are updated depending on the variances on the measurements and the predicted variance of the model states. The discrete internal model of the Kalman filter is given in state space by: x(k + 1) = A(k)x(k) + B(k)u(k) + w(k) z(k) = C(k)x(k) + v(k) In this equation the x is the state vector, the u the control vector, the z is the output vector, w is the system noise and v is the measurement noise. The noise sources are assumed to be white. If this is not true the noise can be coloured using augmented states. The covariance matrix of the system noise equals E{ww T } = Q while the covariance matrix of the measurement noise is given as E{vv T } = R. The crosscovariance matrix between these is assumed to be zero. The error covariance matrix is denoted by P ee. This is the variance of the difference between the state and the measurements. With this internal model a prediction can be made for the next state and the next error covariance. This one-step ahead prediction is given as: x(k k 1) = A(k 1)x(k 1 k 1) + B(k 1)u(k 1) P ee (k k 1) = A(k 1)P ee (k 1 k 1)A T (k 1) + Q(k 1) The estimated state vector is a combination of the predicted state vector based on the internal model and the measured state vector. The Kalman gain matrix M gives a measure for the difference in uncertainties in predicted and measured state vectors, that is based on the innovation matrix S. S is a measure for the total uncertainty: S(k) = C(k)P ee (k k 1)C T (k) + R(k) M(k) = P ee (k k 1)C T (k)s 1 (k)

5 Proc. of Mechatronics 22, University of Twente, June 22 If a new measurement, z, becomes available, the states and the error matrix are updated as follows x(k k) = x(k k 1) + M(k) ( z(k) C(k)x(k k 1)) P ee (k k) = P ee (k k 1) M(k)S(k)M T (k) The internal model that is used in the Kalman filter is a kinematic model. The knowledge that is inserted is that the derivative of the position is the velocity and the derivative of the velocity is the acceleration. A physical model is not used to increase the portability. The measurement should give results according to its specifications for each mass independent of the setup that is used. The inclusion of model knowledge will improve the accuracy. The discrete internal model is given as: A = 1 T s 1 2 T 2 s 1 T s 1, B =, C = [ 1 1 Note the matrix B which consists of only zeros. This is done because the control signal is assumed to be unknown. If the desired acceleration is present from the reference profile, this can be used in the input matrix B. But in our case we assume not to posses this knowledge. This results that for this model all alterations in accelerations are due to the system noise. The covariance matrix Q should get such a value that the filter can react fast enough on changes. The value of this matrix is determined in the next section by simulation. The covariance of the final estimate depends on the variance of the measurements and thus on the chosen sensors. The accuracy of the position has to be 5 µm while the velocity should be estimated within the 1.7 mm/s of the true value. These specifications state that the maximum absolute error is bound. However, the noise sources and the covariance matrices are given as variances. With a given variance, a bound on the absolute error can never be given with absolute certainty. Therefore we interpret the given specifications such that the standard deviation of the accuracies should be below the given values. To estimate the variance allowed on the sensors that will give this accuracy, a set of simulations is performed. 3 Hardware requirements In the previous section it was shown how the different sensor signals were combined to get an estimate of the position and the velocity. In this section the requirements on the hardware are determined. First the allowable variances of the measurement signals that serve as input of the Kalman filter are determined. The position that is used by the Kalman filter is also the combination of several Hall sensors. In the second part of this section the number of required Hall sensors is calculated. 3.1 Variances Unfortunately all sensor outputs are corrupted with noise. In this section we shall determine how large the variance of the noise on the output of the position and acceleration measurement are allowed to be such that the estimated position and velocity is still accurate enough. The variance of the estimated variables is calculated for a set of different variances on the measured variables. The variance of the estimation is determined by calculation ]

6 Proc. of Mechatronics 22, University of Twente, June 22 the error covariance matrix P ee in steady state which can be done using the Riccati equations. The variances of the estimated variables are the diagonal elements of this matrix. The standard deviation of the position estimation is given in figure 4 and the standard deviation of the velocity estimation is given in figure 5. In these figures the standard deviation is plotted as function of the standard deviation of the acceleration measurement. This is done for several values of the standard deviation of the position measurement. The horizontal line denotes the required accuracy. The exchangeability between variance on the position and acceleration measurement can be observed. The calculations are made in the steady state situation. The variance on the sensors is chosen well below the required value. The choice of the sensor variances is given by the diamond in the figures. This is done to play it save and because no control signal is included in the model so the sensors should have to give good measurements to follow fast changes. The cost of the sensors is kept in mind while making this choice. The standard deviation on the measurement is given by: σˆx = [m] σ a = [m/s 2 ] This will result in a steady state deviation of: σˆx σˆv = [m] = [m/s] With these variances the matrix R is given as: [ ] R = 1 3. Next to the calculation of the matrix R the matrix Q should be given a value. This has nothing to do with the hardware selection, but it can be determined during the same simulations for the testing of the measurement setup. This matrix gives the variances on the system noise. Only the acceleration is influenced by the system noise and therefore only the (3,3)-element of this matrix has a value unequal to zero. The change in acceleration is only due to the system noise because the control signal was not available for the internal model. This means that the system noise should be chosen such that this noise is capable of driving the model to the same accelerations as the physical system. If the system noise is chosen too small, the filter will not be capable to estimate the position fast enough and a phase lag will occur. On the other hand, if this value is too high, the noise of the acceleration sensor will not be filtered and is assumed to be the true value of the acceleration. A compromise has to be found. Through simulations the variance of the system noise is determined. The motion that was performed consisted of several sines such that the maximal acceleration was 1 m/s 2 while the maximum velocity was 2 m/s. A reasonable compromise between phase lag and noise was to set the variance on 1. With the given matrices a set of simulations was performed to test if the determined co-variance matrices gave the correct behaviour. The first simulation was to estimate the velocity and the position while standing still. Both estimations were located for most of the time within their predicted standard deviation, as expected. The second simulation was a simulation were the acceleration and the velocity of the motion were at their maximum value of 2 m/s and 1 m/s 2. In this case the maximal error in the

7 Proc. of Mechatronics 22, University of Twente, June σˆx [m] 1 6 σˆx = 1 3 σˆx = 1 4 σˆx = 1 5 σˆx = σˆx = σˆv [m/s] 1 4 σˆx = 1 3 σˆx = σ a [m/s 2 ] Figure 4: The standard deviation of the position estimate for different set of sensors σ a [m/s 2 ] Figure 5: The standard deviation of the position estimate for different set of sensors velocity estimate was (1 2 m/s). The estimation lagged behind and this also resulted in large position errors (36 µm). This error occurred at the highest jerk. If the jerk was limited to remain within 25 m/s 3 the specifications were met. It should be noted that the velocity estimation of the present controller gave worse results. 3.2 Number of Hall sensors The position measurement ˆx is the result of combining the outputs of the Hall sensors. Several Hall sensors are used for this measurement. If the sensors are placed close to the magnets, they are saturated most of the time, but if they are not saturated, the position sensitivity is high. This was already illustrated in figure 3. The closer the sensors are placed by the magnets, the larger the position sensitivity and the shorter the time they are out of saturation. In this paragraph the number of sensors is calculated to achieve the required resolution of 5 µm. The number of sensors depends on the noise of the sensors and the number of bits used in the AD-card. If no noise is present on the sensor output, any resolution could be achieved if enough bits were used for the conversion. However noise is present and it should not introduce errors that are larger than 5 µm for each position of the motor. The output of one Hall sensor can be written as: ( ) 2πx y = 2.5 sin + ɛ [V] p in which p is the pitch of the magnets and ɛ is the noise that is measured to have a standard deviation of 6 µv. The x is the displacement and the amplitude of the output was 2.5 V. The worst spatial sensitivity occurs right above a magnet because the tangent is horizontal there (fig. 2). To estimate the position within 5µm the deviation to the right or the left is only allowed to be 2.5µm. By moving 2.5 µm to the right we find a change of the output of ( 2π y = sin π ) = 2.1 [µv] 2 The standard deviation of the sensor is 6 µv while a change of 2.1 µv gives already rise to a deviation of 2.5 µm. It is clear that the output noise is too large and a larger spatial sensitivity is required.

8 Proc. of Mechatronics 22, University of Twente, June 22 If two sensors are used that are out of saturation successively, the output of these can be given by: 2.5 ( ) 2πx 2 sin p [V] if y y 1 = ( ( )) πx 2.5sign sin p [V] otherwise 2.5 ( ) 2πx 2 sin p y 2 = + 1π 2 [V] if y ( ( )) πx 2.5sign sin [V] otherwise p + 1π 2 The worst sensitivity occurs if one of the sensors is just out of saturation or is just about to enter. A deviation of 2.5 µm would result in a change in the output of: y 1 = π sin( π ) = 3.2 [mv] 4 So in the worst position of the motor, a change in position of 2.5 µm correspond to a change in the output signal of 3.2 mv. This is about 5 times the standard deviation of the sensor noise, and it is therefore unlikely that the sensor noise will introduce measurement errors that are larger than 5 µm. The AD-card should be able to detect the changes of 2.5 µm and should therefore have a maximal quantization step of 3.2 mv and have a reach from to 5 V. This requires a minimum of 11 bits. A set of two Hall sensors read by AD card with 11 bits should be enough to measure the position within 5µm. However, to play it save and to increase the accuracy, four sensors are used with 12-bits AD-converters. 4 Experiments Several experiments were performed on a linear motor that is present in our laboratory. This motor was extended with four Hall sensors that were displaced relative to each other for the measurement of the magnetic field such that one was always out of saturation. Furthermore, two acceleration sensors were mounted on the motor. One was mounted on the moving part while the other was mounted on the stationary part. All these signals were read by a computer through a 12-bit AD-card at a sample frequency of 4 KHz. Three experiments were performed. The first was the estimation of the position and the velocity while standing still. The second was the estimation of the position and the velocity at a constant velocity of 1 m/s and the third was the estimation while accelerating at 5 m/s 2. These experiments were performed in closed loop and the used measurement device for the feedback was still the optical ruler that was already present in the setup. The optical ruler is also used as the true value of the position for comparison reasons although it has a time delay. The position and the velocity estimation based on the acceleration measurement and the Hall outputs was done by the gathered data off-line. The estimation of the variables was done as if the data came in per sample and no techniques were used that could not be used in a real-time setting like anti-causal filtering. First a set of experiments were performed to estimate the time delays in the system and the sensor biases. These estimates were used to compensate for these effects while estimating the position and velocity.

9 Proc. of Mechatronics 22, University of Twente, June Position error [m] Velocity error [m/s] Figure 6: Steady state position error Figure 7: Steady state estimate error Position error [m] 1-1 Velocity error [m/s] Figure 8: Position estimate error while Figure 9: Velocity estimate error while moving with constant velocity moving with constant velocity The error in the position estimate and the velocity estimate in the steady state case are given in figure 6 and 7. The bias in the position estimate is 3.3 µm while the standard deviation is only.6 µm. The bias in the velocity estimate is.7 mm/s while the standard deviation is.13 mm/s. These biases are introduced by a bias in the acceleration measurement. The steady state estimation stays within the specifications. The error in the position and velocity estimate while moving with a constant velocity are given in the figures 8 and 9. It can be observed that a periodic error is present, with a period corresponding to the magnet spacing. This effect is assumed to originate for differences in time delays. Next to the periodic error, the bias of the acceleration sensor can still be observed. The specifications are almost met. The error in the position and velocity estimate while moving with a constant acceleration is given in the figures 1 and 11. The same periodic error as with the constant velocity can be observed. The amplitude of this error grows as the motor is moving faster which can supports the hypothesis that the periodic error is introduced by due to time delays, but it can also be contributed to the magnetic field generated by the motor coils. The specifications are not met, although it is close.

10 Proc. of Mechatronics 22, University of Twente, June Position error [m] Velocity error [m/s] Figure 1: Position estimate error while moving with constant acceleration 5 Conclusions Figure 11: Velocity estimate error while moving with constant acceleration In this paper a method is presented that uses functional interaction and sensor fusion to estimate the position and the velocity of a linear motor. This measurement setup makes it possible to omit the expensive optical ruler and use low cost acceleration sensors and Hall sensors. By using the magnets that are already present in the linear motor to generate the driving force no ruler has to be placed next to the motor which makes this setup constructual attractive. The specifications were met in the steady state case, slightly missed while moving with a constant velocity of 1 m/s and not met while accelerating with constant acceleration. However it is a most promising technique for the replacement of the optical ruler. It is expected that the performance can be easily increased such that it will meet the specifications if the time delays are compensated for as for the motor currents. References Gieras, J. & Piech, Z. (2), Linear Synchronous Motors, Transportation and Automation Systems, CRC Press, Boca Raton, Florida. Ljung, L. (1999), System Identification, Theory for the user, PTR Prentice-Hall information and system sciences series, 2nd edn, Prentice-Hall PTR, Upper Saddle River, New Jersey. Lou, R. & Kay, M. (1992), Data Fusion in Robotics and Machine Intelligence, Academic Press, Inc., chapter Data Fusion and Sensor Integration: State-of-the-art 199 s, pp

Enhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm

Enhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm 1 Enhancing the SNR of the Fiber Optic Rotation Sensor using the LMS Algorithm Hani Mehrpouyan, Student Member, IEEE, Department of Electrical and Computer Engineering Queen s University, Kingston, Ontario,

More information

POTENTIAL OF STATE-FEEDBACK CONTROL FOR MACHINE TOOLS DRIVES

POTENTIAL OF STATE-FEEDBACK CONTROL FOR MACHINE TOOLS DRIVES POTENTIAL OF STATE-FEEDBACK CONTROL FOR MACHINE TOOLS DRIVES L. Novotny 1, P. Strakos 1, J. Vesely 1, A. Dietmair 2 1 Research Center of Manufacturing Technology, CTU in Prague, Czech Republic 2 SW, Universität

More information

The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy

The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy BMI Paper The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy Faculty of Sciences VU University Amsterdam De Boelelaan 1081 1081 HV Amsterdam Netherlands Author: R.D.R.

More information

dspace DSP DS-1104 based State Observer Design for Position Control of DC Servo Motor

dspace DSP DS-1104 based State Observer Design for Position Control of DC Servo Motor dspace DSP DS-1104 based State Observer Design for Position Control of DC Servo Motor Jaswandi Sawant, Divyesh Ginoya Department of Instrumentation and control, College of Engineering, Pune. ABSTRACT This

More information

HYDRAULIC ARM MODELING VIA MATLAB SIMHYDRAULICS

HYDRAULIC ARM MODELING VIA MATLAB SIMHYDRAULICS Engineering MECHANICS, Vol. 16, 2009, No. 4, p. 287 296 287 HYDRAULIC ARM MODELING VIA MATLAB SIMHYDRAULICS Stanislav Věchet, Jiří Krejsa* System modeling is a vital tool for cost reduction and design

More information

Marine Technology Society

Marine Technology Society Marine Technology Society Dynamic Positioning Conference 21-22 October, 1997 Session 9 Control Systems Improved DP Performance in Deep Water Operations Through Advanced Reference System Processing and

More information

Component Ordering in Independent Component Analysis Based on Data Power

Component Ordering in Independent Component Analysis Based on Data Power Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals

More information

Active Vibration Isolation of an Unbalanced Machine Spindle

Active Vibration Isolation of an Unbalanced Machine Spindle UCRL-CONF-206108 Active Vibration Isolation of an Unbalanced Machine Spindle D. J. Hopkins, P. Geraghty August 18, 2004 American Society of Precision Engineering Annual Conference Orlando, FL, United States

More information

Time Series and Forecasting

Time Series and Forecasting Chapter 22 Page 1 Time Series and Forecasting A time series is a sequence of observations of a random variable. Hence, it is a stochastic process. Examples include the monthly demand for a product, the

More information

Encoders for Linear Motors in the Electronics Industry

Encoders for Linear Motors in the Electronics Industry Technical Information Encoders for Linear Motors in the Electronics Industry The semiconductor industry and automation technology increasingly require more precise and faster machines in order to satisfy

More information

Using angular speed measurement with Hall effect sensors to observe grinding operation with flexible robot.

Using angular speed measurement with Hall effect sensors to observe grinding operation with flexible robot. Using angular speed measurement with Hall effect sensors to observe grinding operation with flexible robot. François Girardin 1, Farzad Rafieian 1, Zhaoheng Liu 1, Marc Thomas 1 and Bruce Hazel 2 1 Laboratoire

More information

Dually Fed Permanent Magnet Synchronous Generator Condition Monitoring Using Stator Current

Dually Fed Permanent Magnet Synchronous Generator Condition Monitoring Using Stator Current Summary Dually Fed Permanent Magnet Synchronous Generator Condition Monitoring Using Stator Current Joachim Härsjö, Massimo Bongiorno and Ola Carlson Chalmers University of Technology Energi och Miljö,

More information

Series: IDAM Servo Drive E Digital Motor Drive - DMD-078.12

Series: IDAM Servo Drive E Digital Motor Drive - DMD-078.12 Series: IDAM Servo Drive E Digital Motor Drive - DMD-078.12 inside Integrated amplifiers for 8 single-phase motors, 4 two-phases motors or 4 three-phases motors or combinations of them in one device Position

More information

Introduction to Robotics Analysis, Systems, Applications

Introduction to Robotics Analysis, Systems, Applications Introduction to Robotics Analysis, Systems, Applications Saeed B. Niku Mechanical Engineering Department California Polytechnic State University San Luis Obispo Technische Urw/carsMt Darmstadt FACHBEREfCH

More information

Figure 1. The Ball and Beam System.

Figure 1. The Ball and Beam System. BALL AND BEAM : Basics Peter Wellstead: control systems principles.co.uk ABSTRACT: This is one of a series of white papers on systems modelling, analysis and control, prepared by Control Systems Principles.co.uk

More information

Acceleration levels of dropped objects

Acceleration levels of dropped objects Acceleration levels of dropped objects cmyk Acceleration levels of dropped objects Introduction his paper is intended to provide an overview of drop shock testing, which is defined as the acceleration

More information

Data Sensor Fusion for Autonomous Robotics

Data Sensor Fusion for Autonomous Robotics 19 Data Sensor Fusion for Autonomous Robotics Özer Çiftçioğlu and Sevil Sariyildiz Delft University of echnology, Faculty of Architecture, Delft he Netherlands 1. Introduction Multi-sensory information

More information

Bipolar Transistor Amplifiers

Bipolar Transistor Amplifiers Physics 3330 Experiment #7 Fall 2005 Bipolar Transistor Amplifiers Purpose The aim of this experiment is to construct a bipolar transistor amplifier with a voltage gain of minus 25. The amplifier must

More information

Proof of the conservation of momentum and kinetic energy

Proof of the conservation of momentum and kinetic energy Experiment 04 Proof of the conservation of momentum and kinetic energy By Christian Redeker 27.10.2007 Contents 1.) Hypothesis...3 2.) Diagram...7 3.) Method...7 3.1) Apparatus...7 3.2) Procedure...7 4.)

More information

Cancellation of Load-Regulation in Low Drop-Out Regulators

Cancellation of Load-Regulation in Low Drop-Out Regulators Cancellation of Load-Regulation in Low Drop-Out Regulators Rajeev K. Dokania, Student Member, IEE and Gabriel A. Rincόn-Mora, Senior Member, IEEE Georgia Tech Analog Consortium Georgia Institute of Technology

More information

Understanding and Applying Kalman Filtering

Understanding and Applying Kalman Filtering Understanding and Applying Kalman Filtering Lindsay Kleeman Department of Electrical and Computer Systems Engineering Monash University, Clayton 1 Introduction Objectives: 1. Provide a basic understanding

More information

Confidence Intervals for One Standard Deviation Using Standard Deviation

Confidence Intervals for One Standard Deviation Using Standard Deviation Chapter 640 Confidence Intervals for One Standard Deviation Using Standard Deviation Introduction This routine calculates the sample size necessary to achieve a specified interval width or distance from

More information

Moving Magnet Actuator MI FFA series

Moving Magnet Actuator MI FFA series Moving Magnet Actuator MI FFA series The moving magnet MI-FFA series actuators are a line of actuators designed to be a true alternative for pneumatic cylinders. The actuators incorporate an ISO 6432 interface

More information

Automatic Detection of Emergency Vehicles for Hearing Impaired Drivers

Automatic Detection of Emergency Vehicles for Hearing Impaired Drivers Automatic Detection of Emergency Vehicles for Hearing Impaired Drivers Sung-won ark and Jose Trevino Texas A&M University-Kingsville, EE/CS Department, MSC 92, Kingsville, TX 78363 TEL (36) 593-2638, FAX

More information

Experimental Uncertainties (Errors)

Experimental Uncertainties (Errors) Experimental Uncertainties (Errors) Sources of Experimental Uncertainties (Experimental Errors): All measurements are subject to some uncertainty as a wide range of errors and inaccuracies can and do happen.

More information

Time Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication

Time Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication Time Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication Thomas Reilly Data Physics Corporation 1741 Technology Drive, Suite 260 San Jose, CA 95110 (408) 216-8440 This paper

More information

Force/position control of a robotic system for transcranial magnetic stimulation

Force/position control of a robotic system for transcranial magnetic stimulation Force/position control of a robotic system for transcranial magnetic stimulation W.N. Wan Zakaria School of Mechanical and System Engineering Newcastle University Abstract To develop a force control scheme

More information

Magnetic Field Modeling of Halbach Permanent Magnet Array

Magnetic Field Modeling of Halbach Permanent Magnet Array Magnetic Field Modeling of Halbach Permanent Magnet Array Shengguo Zhang *1, Kai Wang 1, Xiaoping Dang 2 School of Electrical Engineering, Northwest University for Nationalities, Lanzhou, China School

More information

DIGITAL-TO-ANALOGUE AND ANALOGUE-TO-DIGITAL CONVERSION

DIGITAL-TO-ANALOGUE AND ANALOGUE-TO-DIGITAL CONVERSION DIGITAL-TO-ANALOGUE AND ANALOGUE-TO-DIGITAL CONVERSION Introduction The outputs from sensors and communications receivers are analogue signals that have continuously varying amplitudes. In many systems

More information

Advantages of Auto-tuning for Servo-motors

Advantages of Auto-tuning for Servo-motors Advantages of for Servo-motors Executive summary The same way that 2 years ago computer science introduced plug and play, where devices would selfadjust to existing system hardware, industrial motion control

More information

E. K. A. ADVANCED PHYSICS LABORATORY PHYSICS 3081, 4051 NUCLEAR MAGNETIC RESONANCE

E. K. A. ADVANCED PHYSICS LABORATORY PHYSICS 3081, 4051 NUCLEAR MAGNETIC RESONANCE E. K. A. ADVANCED PHYSICS LABORATORY PHYSICS 3081, 4051 NUCLEAR MAGNETIC RESONANCE References for Nuclear Magnetic Resonance 1. Slichter, Principles of Magnetic Resonance, Harper and Row, 1963. chapter

More information

Frequency Response of Filters

Frequency Response of Filters School of Engineering Department of Electrical and Computer Engineering 332:224 Principles of Electrical Engineering II Laboratory Experiment 2 Frequency Response of Filters 1 Introduction Objectives To

More information

UNIT 1 INTRODUCTION TO NC MACHINE TOOLS

UNIT 1 INTRODUCTION TO NC MACHINE TOOLS UNIT 1 INTRODUCTION TO NC MACHINE TOOLS Structure 1.1 Introduction Objectives 1.2 NC Machines 1.2.1 Types of NC Machine 1.2.2 Controlled Axes 1.2.3 Basic Components of NC Machines 1.2.4 Problems with Conventional

More information

A Determination of g, the Acceleration Due to Gravity, from Newton's Laws of Motion

A Determination of g, the Acceleration Due to Gravity, from Newton's Laws of Motion A Determination of g, the Acceleration Due to Gravity, from Newton's Laws of Motion Objective In the experiment you will determine the cart acceleration, a, and the friction force, f, experimentally for

More information

Measuring Line Edge Roughness: Fluctuations in Uncertainty

Measuring Line Edge Roughness: Fluctuations in Uncertainty Tutor6.doc: Version 5/6/08 T h e L i t h o g r a p h y E x p e r t (August 008) Measuring Line Edge Roughness: Fluctuations in Uncertainty Line edge roughness () is the deviation of a feature edge (as

More information

A Reliability Point and Kalman Filter-based Vehicle Tracking Technique

A Reliability Point and Kalman Filter-based Vehicle Tracking Technique A Reliability Point and Kalman Filter-based Vehicle Tracing Technique Soo Siang Teoh and Thomas Bräunl Abstract This paper introduces a technique for tracing the movement of vehicles in consecutive video

More information

In order to describe motion you need to describe the following properties.

In order to describe motion you need to describe the following properties. Chapter 2 One Dimensional Kinematics How would you describe the following motion? Ex: random 1-D path speeding up and slowing down In order to describe motion you need to describe the following properties.

More information

Transistor Amplifiers

Transistor Amplifiers Physics 3330 Experiment #7 Fall 1999 Transistor Amplifiers Purpose The aim of this experiment is to develop a bipolar transistor amplifier with a voltage gain of minus 25. The amplifier must accept input

More information

HITACHI INVERTER SJ/L100/300 SERIES PID CONTROL USERS GUIDE

HITACHI INVERTER SJ/L100/300 SERIES PID CONTROL USERS GUIDE HITACHI INVERTER SJ/L1/3 SERIES PID CONTROL USERS GUIDE After reading this manual, keep it for future reference Hitachi America, Ltd. HAL1PID CONTENTS 1. OVERVIEW 3 2. PID CONTROL ON SJ1/L1 INVERTERS 3

More information

Conservation of Energy Physics Lab VI

Conservation of Energy Physics Lab VI Conservation of Energy Physics Lab VI Objective This lab experiment explores the principle of energy conservation. You will analyze the final speed of an air track glider pulled along an air track by a

More information

18.6.1 Terms concerned with internal quality control procedures

18.6.1 Terms concerned with internal quality control procedures 18.6.1 Terms concerned with internal quality control procedures Quality assurance in analytical laboratories Quality assurance is the essential organisational infrastructure that underlies all reliable

More information

SOLID MECHANICS TUTORIAL MECHANISMS KINEMATICS - VELOCITY AND ACCELERATION DIAGRAMS

SOLID MECHANICS TUTORIAL MECHANISMS KINEMATICS - VELOCITY AND ACCELERATION DIAGRAMS SOLID MECHANICS TUTORIAL MECHANISMS KINEMATICS - VELOCITY AND ACCELERATION DIAGRAMS This work covers elements of the syllabus for the Engineering Council exams C105 Mechanical and Structural Engineering

More information

SIGNAL GENERATORS and OSCILLOSCOPE CALIBRATION

SIGNAL GENERATORS and OSCILLOSCOPE CALIBRATION 1 SIGNAL GENERATORS and OSCILLOSCOPE CALIBRATION By Lannes S. Purnell FLUKE CORPORATION 2 This paper shows how standard signal generators can be used as leveled sine wave sources for calibrating oscilloscopes.

More information

Whitepaper. Image stabilization improving camera usability

Whitepaper. Image stabilization improving camera usability Whitepaper Image stabilization improving camera usability Table of contents 1. Introduction 3 2. Vibration Impact on Video Output 3 3. Image Stabilization Techniques 3 3.1 Optical Image Stabilization 3

More information

ELECTRICAL ENGINEERING

ELECTRICAL ENGINEERING EE ELECTRICAL ENGINEERING See beginning of Section H for abbreviations, course numbers and coding. The * denotes labs which are held on alternate weeks. A minimum grade of C is required for all prerequisite

More information

The Periodic Moving Average Filter for Removing Motion Artifacts from PPG Signals

The Periodic Moving Average Filter for Removing Motion Artifacts from PPG Signals International Journal The of Periodic Control, Moving Automation, Average and Filter Systems, for Removing vol. 5, no. Motion 6, pp. Artifacts 71-76, from December PPG s 27 71 The Periodic Moving Average

More information

Simulation and Analysis of Parameter Identification Techniques for Induction Motor Drive

Simulation and Analysis of Parameter Identification Techniques for Induction Motor Drive International Journal of Electronic and Electrical Engineering. ISSN 0974-2174 Volume 7, Number 10 (2014), pp. 1027-1035 International Research Publication House http://www.irphouse.com Simulation and

More information

Thermodynamic efficiency of an actuator that provides the mechanical movement for the driven equipments:

Thermodynamic efficiency of an actuator that provides the mechanical movement for the driven equipments: 1. Introduction 1.1. Industry Automation Industry automation is the term that describes a vital development programme of a production community where the project engineers build up automated manufacturing

More information

JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions

JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions Edith Cowan University Research Online ECU Publications Pre. JPEG compression of monochrome D-barcode images using DCT coefficient distributions Keng Teong Tan Hong Kong Baptist University Douglas Chai

More information

2x + y = 3. Since the second equation is precisely the same as the first equation, it is enough to find x and y satisfying the system

2x + y = 3. Since the second equation is precisely the same as the first equation, it is enough to find x and y satisfying the system 1. Systems of linear equations We are interested in the solutions to systems of linear equations. A linear equation is of the form 3x 5y + 2z + w = 3. The key thing is that we don t multiply the variables

More information

Section 3. Sensor to ADC Design Example

Section 3. Sensor to ADC Design Example Section 3 Sensor to ADC Design Example 3-1 This section describes the design of a sensor to ADC system. The sensor measures temperature, and the measurement is interfaced into an ADC selected by the systems

More information

General model of a structure-borne sound source and its application to shock vibration

General model of a structure-borne sound source and its application to shock vibration General model of a structure-borne sound source and its application to shock vibration Y. Bobrovnitskii and T. Tomilina Mechanical Engineering Research Institute, 4, M. Kharitonievky Str., 101990 Moscow,

More information

Diagnosis of multi-operational machining processes through variation propagation analysis

Diagnosis of multi-operational machining processes through variation propagation analysis Robotics and Computer Integrated Manufacturing 18 (2002) 233 239 Diagnosis of multi-operational machining processes through variation propagation analysis Qiang Huang, Shiyu Zhou, Jianjun Shi* Department

More information

EDUMECH Mechatronic Instructional Systems. Ball on Beam System

EDUMECH Mechatronic Instructional Systems. Ball on Beam System EDUMECH Mechatronic Instructional Systems Ball on Beam System Product of Shandor Motion Systems Written by Robert Hirsch Ph.D. 998-9 All Rights Reserved. 999 Shandor Motion Systems, Ball on Beam Instructional

More information

DCMS DC MOTOR SYSTEM User Manual

DCMS DC MOTOR SYSTEM User Manual DCMS DC MOTOR SYSTEM User Manual release 1.3 March 3, 2011 Disclaimer The developers of the DC Motor System (hardware and software) have used their best efforts in the development. The developers make

More information

3. KINEMATICS IN TWO DIMENSIONS; VECTORS.

3. KINEMATICS IN TWO DIMENSIONS; VECTORS. 3. KINEMATICS IN TWO DIMENSIONS; VECTORS. Key words: Motion in Two Dimensions, Scalars, Vectors, Addition of Vectors by Graphical Methods, Tail to Tip Method, Parallelogram Method, Negative Vector, Vector

More information

THERMAL ANEMOMETRY ELECTRONICS, SOFTWARE AND ACCESSORIES

THERMAL ANEMOMETRY ELECTRONICS, SOFTWARE AND ACCESSORIES TSI and TSI logo are registered trademarks of TSI Incorporated. SmartTune is a trademark of TSI Incorporated. THERMAL ANEMOMETRY ELECTRONICS, SOFTWARE AND ACCESSORIES IFA 300 Constant Temperature Anemometry

More information

Course 8. An Introduction to the Kalman Filter

Course 8. An Introduction to the Kalman Filter Course 8 An Introduction to the Kalman Filter Speakers Greg Welch Gary Bishop Kalman Filters in 2 hours? Hah! No magic. Pretty simple to apply. Tolerant of abuse. Notes are a standalone reference. These

More information

Intelligent Flexible Automation

Intelligent Flexible Automation Intelligent Flexible Automation David Peters Chief Executive Officer Universal Robotics February 20-22, 2013 Orlando World Marriott Center Orlando, Florida USA Trends in AI and Computing Power Convergence

More information

Mechanics 1: Conservation of Energy and Momentum

Mechanics 1: Conservation of Energy and Momentum Mechanics : Conservation of Energy and Momentum If a certain quantity associated with a system does not change in time. We say that it is conserved, and the system possesses a conservation law. Conservation

More information

PC BASED PID TEMPERATURE CONTROLLER

PC BASED PID TEMPERATURE CONTROLLER PC BASED PID TEMPERATURE CONTROLLER R. Nisha * and K.N. Madhusoodanan Dept. of Instrumentation, Cochin University of Science and Technology, Cochin 22, India ABSTRACT: A simple and versatile PC based Programmable

More information

The simulation of machine tools can be divided into two stages. In the first stage the mechanical behavior of a machine tool is simulated with FEM

The simulation of machine tools can be divided into two stages. In the first stage the mechanical behavior of a machine tool is simulated with FEM 1 The simulation of machine tools can be divided into two stages. In the first stage the mechanical behavior of a machine tool is simulated with FEM tools. The approach to this simulation is different

More information

DESIGN PROCEDURE FOR A LEARNING FEED-FORWARD CONTROLLER

DESIGN PROCEDURE FOR A LEARNING FEED-FORWARD CONTROLLER Proc. 1 st IFAC Conf. on Mechatronic Systems (Darmstadt, Germany, 18 0 Sept. 000), R. Isermann (ed.), VDI/VDE Gesellschaft Mess-und Automatiserungstechnik GMA, Düsseldorf, Germany. DESIGN PROCEDURE FOR

More information

The Effect of Network Cabling on Bit Error Rate Performance. By Paul Kish NORDX/CDT

The Effect of Network Cabling on Bit Error Rate Performance. By Paul Kish NORDX/CDT The Effect of Network Cabling on Bit Error Rate Performance By Paul Kish NORDX/CDT Table of Contents Introduction... 2 Probability of Causing Errors... 3 Noise Sources Contributing to Errors... 4 Bit Error

More information

FREQUENCY RESPONSE ANALYZERS

FREQUENCY RESPONSE ANALYZERS FREQUENCY RESPONSE ANALYZERS Dynamic Response Analyzers Servo analyzers When you need to stabilize feedback loops to measure hardware characteristics to measure system response BAFCO, INC. 717 Mearns Road

More information

Content. Professur für Steuerung, Regelung und Systemdynamik. Lecture: Vehicle Dynamics Tutor: T. Wey Date: 01.01.08, 20:11:52

Content. Professur für Steuerung, Regelung und Systemdynamik. Lecture: Vehicle Dynamics Tutor: T. Wey Date: 01.01.08, 20:11:52 1 Content Overview 1. Basics on Signal Analysis 2. System Theory 3. Vehicle Dynamics Modeling 4. Active Chassis Control Systems 5. Signals & Systems 6. Statistical System Analysis 7. Filtering 8. Modeling,

More information

Drivetech, Inc. Innovations in Motor Control, Drives, and Power Electronics

Drivetech, Inc. Innovations in Motor Control, Drives, and Power Electronics Drivetech, Inc. Innovations in Motor Control, Drives, and Power Electronics Dal Y. Ohm, Ph.D. - President 25492 Carrington Drive, South Riding, Virginia 20152 Ph: (703) 327-2797 Fax: (703) 327-2747 ohm@drivetechinc.com

More information

Robot coined by Karel Capek in a 1921 science-fiction Czech play

Robot coined by Karel Capek in a 1921 science-fiction Czech play Robotics Robot coined by Karel Capek in a 1921 science-fiction Czech play Definition: A robot is a reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices

More information

System Modeling and Control for Mechanical Engineers

System Modeling and Control for Mechanical Engineers Session 1655 System Modeling and Control for Mechanical Engineers Hugh Jack, Associate Professor Padnos School of Engineering Grand Valley State University Grand Rapids, MI email: jackh@gvsu.edu Abstract

More information

Precision Diode Rectifiers

Precision Diode Rectifiers by Kenneth A. Kuhn March 21, 2013 Precision half-wave rectifiers An operational amplifier can be used to linearize a non-linear function such as the transfer function of a semiconductor diode. The classic

More information

Linear Motion System: Transport and positioning for demanding applications

Linear Motion System: Transport and positioning for demanding applications Linear Motion System: Transport and positioning for demanding applications 2 The Perfect Concept for a variety of applications The Linear Motion System (LMS) from Rexroth is a unique technical solution

More information

The CUSUM algorithm a small review. Pierre Granjon

The CUSUM algorithm a small review. Pierre Granjon The CUSUM algorithm a small review Pierre Granjon June, 1 Contents 1 The CUSUM algorithm 1.1 Algorithm............................... 1.1.1 The problem......................... 1.1. The different steps......................

More information

Chapter 3 Non-parametric Models for Magneto-Rheological Dampers

Chapter 3 Non-parametric Models for Magneto-Rheological Dampers Chapter 3 Non-parametric Models for Magneto-Rheological Dampers The primary purpose of this chapter is to present an approach for developing nonparametric models for magneto-rheological (MR) dampers. Upon

More information

Physics Lab Report Guidelines

Physics Lab Report Guidelines Physics Lab Report Guidelines Summary The following is an outline of the requirements for a physics lab report. A. Experimental Description 1. Provide a statement of the physical theory or principle observed

More information

Mobile Robot FastSLAM with Xbox Kinect

Mobile Robot FastSLAM with Xbox Kinect Mobile Robot FastSLAM with Xbox Kinect Design Team Taylor Apgar, Sean Suri, Xiangdong Xi Design Advisor Prof. Greg Kowalski Abstract Mapping is an interesting and difficult problem in robotics. In order

More information

High Accuracy Articulated Robots with CNC Control Systems

High Accuracy Articulated Robots with CNC Control Systems Copyright 2012 SAE International 2013-01-2292 High Accuracy Articulated Robots with CNC Control Systems Bradley Saund, Russell DeVlieg Electroimpact Inc. ABSTRACT A robotic arm manipulator is often an

More information

DINAMIC AND STATIC CENTRE OF PRESSURE MEASUREMENT ON THE FORCEPLATE. F. R. Soha, I. A. Szabó, M. Budai. Abstract

DINAMIC AND STATIC CENTRE OF PRESSURE MEASUREMENT ON THE FORCEPLATE. F. R. Soha, I. A. Szabó, M. Budai. Abstract ACTA PHYSICA DEBRECINA XLVI, 143 (2012) DINAMIC AND STATIC CENTRE OF PRESSURE MEASUREMENT ON THE FORCEPLATE F. R. Soha, I. A. Szabó, M. Budai University of Debrecen, Department of Solid State Physics Abstract

More information

MoveInspect HF HR. 3D measurement of dynamic processes MEASURE THE ADVANTAGE. MoveInspect TECHNOLOGY

MoveInspect HF HR. 3D measurement of dynamic processes MEASURE THE ADVANTAGE. MoveInspect TECHNOLOGY MoveInspect HF HR 3D measurement of dynamic processes MEASURE THE ADVANTAGE MoveInspect TECHNOLOGY MoveInspect HF HR 3D measurement of dynamic processes Areas of application In order to sustain its own

More information

Physical Quantities and Units

Physical Quantities and Units Physical Quantities and Units 1 Revision Objectives This chapter will explain the SI system of units used for measuring physical quantities and will distinguish between vector and scalar quantities. You

More information

Computer Networks and Internets, 5e Chapter 6 Information Sources and Signals. Introduction

Computer Networks and Internets, 5e Chapter 6 Information Sources and Signals. Introduction Computer Networks and Internets, 5e Chapter 6 Information Sources and Signals Modified from the lecture slides of Lami Kaya (LKaya@ieee.org) for use CECS 474, Fall 2008. 2009 Pearson Education Inc., Upper

More information

Procedure for Marine Traffic Simulation with AIS Data

Procedure for Marine Traffic Simulation with AIS Data http://www.transnav.eu the International Journal on Marine Navigation and Safety of Sea Transportation Volume 9 Number 1 March 2015 DOI: 10.12716/1001.09.01.07 Procedure for Marine Traffic Simulation with

More information

Adding Heart to Your Technology

Adding Heart to Your Technology RMCM-01 Heart Rate Receiver Component Product code #: 39025074 KEY FEATURES High Filtering Unit Designed to work well on constant noise fields SMD component: To be installed as a standard component to

More information

Accurate Air Flow Measurement in Electronics Cooling

Accurate Air Flow Measurement in Electronics Cooling Accurate Air Flow Measurement in Electronics Cooling Joachim Preiss, Raouf Ismail Cambridge AccuSense, Inc. E-mail: info@degreec.com Air is the most commonly used medium to remove heat from electronics

More information

Chapter 11 SERVO VALVES. Fluid Power Circuits and Controls, John S.Cundiff, 2001

Chapter 11 SERVO VALVES. Fluid Power Circuits and Controls, John S.Cundiff, 2001 Chapter 11 SERVO VALVES Fluid Power Circuits and Controls, John S.Cundiff, 2001 Servo valves were developed to facilitate the adjustment of fluid flow based on the changes in the load motion. 1 Typical

More information

Modern Physics Laboratory e/m with Teltron Deflection Tube

Modern Physics Laboratory e/m with Teltron Deflection Tube Modern Physics Laboratory e/m with Teltron Deflection Tube Josh Diamond & John Cummings Fall 2010 Abstract The deflection of an electron beam by electric and magnetic fields is observed, and the charge

More information

A Multi-Model Filter for Mobile Terminal Location Tracking

A Multi-Model Filter for Mobile Terminal Location Tracking A Multi-Model Filter for Mobile Terminal Location Tracking M. McGuire, K.N. Plataniotis The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 1 King s College

More information

Mapping an Application to a Control Architecture: Specification of the Problem

Mapping an Application to a Control Architecture: Specification of the Problem Mapping an Application to a Control Architecture: Specification of the Problem Mieczyslaw M. Kokar 1, Kevin M. Passino 2, Kenneth Baclawski 1, and Jeffrey E. Smith 3 1 Northeastern University, Boston,

More information

Calibration of the MASS time constant by simulation

Calibration of the MASS time constant by simulation Calibration of the MASS time constant by simulation A. Tokovinin Version 1.1. July 29, 2009 file: prj/atm/mass/theory/doc/timeconstnew.tex 1 Introduction The adaptive optics atmospheric time constant τ

More information

LOCATION DEPENDENCY OF POSITIONING ERROR IN A 3-AXES CNC MILLING MACHINE

LOCATION DEPENDENCY OF POSITIONING ERROR IN A 3-AXES CNC MILLING MACHINE th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 214) December 12 th 14 th, 214, IIT Guwahati, Assam, India LOCATION DEPENDENCY OF POSITIONING ERROR IN

More information

Practice Test SHM with Answers

Practice Test SHM with Answers Practice Test SHM with Answers MPC 1) If we double the frequency of a system undergoing simple harmonic motion, which of the following statements about that system are true? (There could be more than one

More information

An Introduction to the Kalman Filter

An Introduction to the Kalman Filter An Introduction to the Kalman Filter Greg Welch 1 and Gary Bishop 2 TR 95041 Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill, NC 275993175 Updated: Monday, July 24,

More information

/ Department of Mechanical Engineering. Manufacturing Networks. Warehouse storage: cases or layers? J.J.P. van Heur. Where innovation starts

/ Department of Mechanical Engineering. Manufacturing Networks. Warehouse storage: cases or layers? J.J.P. van Heur. Where innovation starts / Department of Mechanical Engineering Manufacturing Networks Warehouse storage: cases or layers? J.J.P. van Heur Where innovation starts Systems Engineering Group Department of Mechanical Engineering

More information

Optical Fibres. Introduction. Safety precautions. For your safety. For the safety of the apparatus

Optical Fibres. Introduction. Safety precautions. For your safety. For the safety of the apparatus Please do not remove this manual from from the lab. It is available at www.cm.ph.bham.ac.uk/y2lab Optics Introduction Optical fibres are widely used for transmitting data at high speeds. In this experiment,

More information

Robot Perception Continued

Robot Perception Continued Robot Perception Continued 1 Visual Perception Visual Odometry Reconstruction Recognition CS 685 11 Range Sensing strategies Active range sensors Ultrasound Laser range sensor Slides adopted from Siegwart

More information

Linear-Quadratic Optimal Controller 10.3 Optimal Linear Control Systems

Linear-Quadratic Optimal Controller 10.3 Optimal Linear Control Systems Linear-Quadratic Optimal Controller 10.3 Optimal Linear Control Systems In Chapters 8 and 9 of this book we have designed dynamic controllers such that the closed-loop systems display the desired transient

More information

How to Turn an AC Induction Motor Into a DC Motor (A Matter of Perspective) Steve Bowling Application Segments Engineer Microchip Technology, Inc.

How to Turn an AC Induction Motor Into a DC Motor (A Matter of Perspective) Steve Bowling Application Segments Engineer Microchip Technology, Inc. 1 How to Turn an AC Induction Motor Into a DC Motor (A Matter of Perspective) Steve Bowling Application Segments Engineer Microchip Technology, Inc. The territory of high-performance motor control has

More information

LINEAR MOTOR CONTROL IN ACTIVE SUSPENSION SYSTEMS

LINEAR MOTOR CONTROL IN ACTIVE SUSPENSION SYSTEMS LINEAR MOTOR CONTROL IN ACTIVE SUSPENSION SYSTEMS HONCŮ JAROSLAV, HYNIOVÁ KATEŘINA, STŘÍBRSKÝ ANTONÍN Department of Control Engineering, Faculty of Electrical Engineering, Czech Technical University Karlovo

More information

A System for Capturing High Resolution Images

A System for Capturing High Resolution Images A System for Capturing High Resolution Images G.Voyatzis, G.Angelopoulos, A.Bors and I.Pitas Department of Informatics University of Thessaloniki BOX 451, 54006 Thessaloniki GREECE e-mail: pitas@zeus.csd.auth.gr

More information

Synchronization of sampling in distributed signal processing systems

Synchronization of sampling in distributed signal processing systems Synchronization of sampling in distributed signal processing systems Károly Molnár, László Sujbert, Gábor Péceli Department of Measurement and Information Systems, Budapest University of Technology and

More information

SYNCHRONOUS MACHINES

SYNCHRONOUS MACHINES SYNCHRONOUS MACHINES The geometry of a synchronous machine is quite similar to that of the induction machine. The stator core and windings of a three-phase synchronous machine are practically identical

More information