State Variable System Identification through Frequency Domain Techniques
A thesis presented to
the faculty of
the Russ College of Engineering and Technology of Ohio University
In partial fulfillment
of the requirements for the degree
Master of Science
Trevor Joseph Bihl
June 2011
© 2011 Trevor Joseph Bihl. All Rights Reserved.
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This thesis entitled
State Variable System Identification through Frequency Domain Techniques
by
TREVOR JOSEPH BIHL
has been approved for
the School of Electrical Engineering and Computer Science
and the Russ College of Engineering and Technology by
Jerrel R. Mitchell
Professor of Electrical Engineering and Computer Science
Dennis Irwin
Dean, Russ College of Engineering and Technology
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ABSTRACT
BIHL, TREVOR J., M.S., June 2011, Electrical Engineering
State Variable System Identification through Frequency Domain Techniques
Director of Thesis: Jerrel R. Mitchell
The thesis develops, tests and implements a hybrid frequency domain and state space system identification method. A frequency domain least squares system identification algorithm, along with a coherence function technique for eliminating noisy data is used to sequentially develop discrete single-input, multiple-output (SIMO) transfer function models between each input and the outputs. From the transfer function models, difference equations are obtained. Using the difference equations, discrete impulse responses between each input and each output are computed. These impulse responses are then processed by a state space system identification technique to create a minimum order state space multiple-input, multiple-output (MIMO) model. This process is illustrated with a MIMO example and with data from a laboratory facility called
Flexlab.
Approved: ______
Jerrel R. Mitchell
Professor of Electrical Engineering and Computer Science
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DEDICATION
To the memory of my father.
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ACKNOWLEDGEMENTS
I would like to express my gratitude to my advisor, Dr. Jerrel Mitchell, for his dedication, support and guidance. I would also like to thank Drs. Douglas Lawrence, J.
Jim Zhu and Sergiu Aizicovici, for serving on my committee.
I am very much indebted to the direction and advice of Drs. Angie Bukley, Brian
Manhire and William Shepherd; without their influence my life and research path would have been decidedly different.
The support and friendship of Alex, Animesh, Annie, Aoy, Behlul, Bill, Chia-ju,
Craig, Deb, Deng, Golf, Harry, James, Joey, Mark, Pang and Paul and all of the friends I made at Ohio; without whom my life would be significantly boring. A special thanks goes to my good friend caffeine, without whom nothing would have been possible.
Additionally, without the camaraderie and friendship of Lt Col. Dave Ryer at AFIT, this work would have taken a different turn.
In addition, the lifelong support and encouragement of my parents have made this possible and I will forever be grateful for that.
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TABLE OF CONTENTS Page
ABSTRACT ...... 3 DEDICATION ...... 4 ACKNOWLEDGEMENTS ...... 5 CHAPTER 1: INTRODUCTION ...... 12 1.1 Background ...... 12 1.2 Flexlab Testbed Description ...... 15 1.3 System Identification ...... 19 1.4 Organization ...... 23 CHAPTER 2: SYSTEM IDENTIFICATION METHODS ...... 24 2.1 Ordinary Least Squares ...... 25 2.2 Total Least Squares ...... 27 2.3 Least Squares System Identification ...... 29 2.3.1 Time Domain Least Squares System Identification ...... 31 2.3.2 Frequency Domain Least Squares System Identification ...... 33 2.4 State Space System Identification ...... 36 CHAPTER 3: METHODOLOGY AND ANALYSIS WITH CONTRIVED EXAMPLE ...... 42 3.1 Purpose ...... 42 3.2 Applying the Coherence Threshold in SIMO Systems for Frequency Domain Noise Reduction ...... 44 3.3 Extending TFDC to SIMO System Identification ...... 45 3.4 Extending Frequency Domain System Identification to a State-Space Realization ...... 48 3.5 Proof of Concept Testing ...... 49 3.5.1 Simulation Setup ...... 49 3.5.2 Application of Coherence Threshold and TFDC Algorithm ...... 56 3.5.3 Hybrid System ID Through ERA ...... 62 3.5.4 Results Comparison ...... 66 CHAPTER 4: TEST BED DEMONSTRATION RESULTS USING FLEXLAB ...... 72 4.1 System Excitation and Data Collection ...... 72 4.2 Hybrid System Methodology for Flexlab ...... 79
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4.3 Controller Design ...... 86 4.3.1 Digital Low Pass Filtering ...... 86 4.3.2 Optimal Controller Design ...... 87 4.4 Flexlab Simulation in Matlab...... 94 4.5 Flexlab Platform Results ...... 97 4.6 Additional Controller Designs and Results ...... 100 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS ...... 105 REFERENCES ...... 108 APPENDIX: PRIMARY MATLAB DATA ANALYSIS FUNCTIONS ...... 112
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LIST OF FIGURES Page
Figure 1-1: Current Configuration of Flexlab (Strahler, March, 2000) ...... 16
Figure 1-2: Operational View of Flexlab ...... 17
Figure 1-3: Screenshot of LabVIEW Based Flexlab Control Panel ...... 19
Figure 3-1: Bode Plot of Original System and System with Simulated Noise for Input 1 to Output 1 ...... 53
Figure 3-2: Bode Plot of Original System and System with Simulated Noise for Input 1 to Output 2 ...... 54
Figure 3-3: Bode Plot of Original System and System with Simulated Noise for Input 2 to Output 1 ...... 55
Figure 3-4: Bode Plot of Original System and System with Simulated Noise for Input 2 to Output 2 ...... 56
Figure 3-5: Coherence Function and Coherence Threshold Input 1 to Output 1 ...... 57
Figure 3-6: Coherence Function and Coherence Threshold Input 1 to Output 2 ...... 58
Figure 3-7: Coherence Function and Coherence Threshold Input 2 to Output 1 ...... 58
Figure 3-8: Coherence Function and Coherence Threshold Input 2 to Output 2 ...... 59
Figure 3-9: TFDC Estimated Bode Plot of Input 1 to Output 1 ...... 60
Figure 3-10: TFDC Estimated Bode Plot of Input 1 to Output 2 ...... 61
Figure 3-11: TFDC Estimated Bode Plot of Input 2 to Output 1 ...... 61
Figure 3-12: TFDC Estimated Bode Plot of Input 2 to Output 2 ...... 62
Figure 3-13: Hybrid System Bode Plot of Input 1 to Output 1 ...... 64
Figure 3-14: Hybrid System Bode Plot of Input 1 to Output 2 ...... 64
Figure 3-15: Hybrid System Bode Plot of Input 2 to Output 1 ...... 65
Figure 3-16: Hybrid System Bode Plot of Input 2 to Output 2 ...... 65
Figure 3-17: Total Simulated Errors Input 1 to Output 1 ...... 67
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Figure 3-18: Total Simulated Errors Input 1 to Output 2 ...... 68
Figure 3-19: Total Simulated Errors Input 2 to Output 1 ...... 69
Figure 3-20: Total Simulated Errors Input 2 to Output 2 ...... 70
Figure 4-1: Bode Plot of Flexlab for X-Axis Motor to X-Axis PSD ...... 73
Figure 4-2: Bode Plot of Flexlab for X-Axis Motor to Y-Axis PSD ...... 74
Figure 4-3: Bode Plot of Flexlab for Y-Axis Motor to X-Axis PSD ...... 74
Figure 4-4: Bode Plot of Flexlab for Y-Axis Motor to Y-Axis PSD ...... 75
Figure 4-5: Coherence Function and Coherence Threshold for Flexlab X-Axis Motor to X-Axis PSD with Applied Coherence Threshold of 0.3 ...... 76
Figure 4-6: Coherence Function and Coherence Threshold for Flexlab X-Axis Motor to Y-Axis PSD with Notional Coherence Threshold of 0.3 ...... 77
Figure 4-7: Coherence Function and Coherence Threshold for Flexlab Y-Axis Motor to X-Axis PSD with Notional Coherence Threshold of 0.3 ...... 77
Figure 4-8: Coherence Function and Coherence Threshold for Flexlab Y-Axis Motor to Y-Axis PSD with Applied Coherence Threshold of 0.3 ...... 78
Figure 4-9: TFDC Estimated Bode Plot for Flexlab X-Axis Motor to X-Axis PSD ...... 79
Figure 4-10: TFDC Estimated Bode Plot for Flexlab X-Axis Motor to Y-Axis PSD .... 80
Figure 4-11: TFDC Estimated Bode Plot for Flexlab Y-Axis Motor to X-Axis PSD .... 80
Figure 4-12: TFDC Estimated Bode Plot for Flexlab Y-Axis Motor to Y-Axis PSD .... 81
Figure 4-13: Flexlab Impulse Response Data X input X output ...... 82
Figure 4-14: Flexlab Impulse Response Data X input Y output ...... 82
Figure 4-15: Flexlab Impulse Response Data Y input X output ...... 83
Figure 4-16: Flexlab Impulse Response Data Y input Y output ...... 83
Figure 4-17: Hybrid System Estimated Bode Plot for Flexlab X-Axis Motor to X-Axis PSD ...... 84
Figure 4-18: Hybrid System Estimated Bode Plot for Flexlab X-Axis Motor to Y-Axis PSD ...... 85
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Figure 4-19: Hybrid System Estimated Bode Plot for Flexlab Y-Axis Motor to X-Axis PSD ...... 85
Figure 4-20: Hybrid System Estimated Bode Plot for Flexlab Y-Axis Motor to Y-Axis PSD ...... 86
Figure 4-21: Hybrid Open Loop Bode Plot of Digital Low Pass Filter ...... 87
Figure 4-22: Bode Plot of Flexlab for X-Axis Motor to X-Axis PSD ...... 89
Figure 4-23: Bode Plot of Flexlab for X-Axis Motor to Y-Axis PSD ...... 90
Figure 4-24: Bode Plot of Flexlab for Y-Axis Motor to X-Axis PSD ...... 90
Figure 4-25: Bode Plot of Flexlab for Y-Axis Motor to Y-Axis PSD ...... 91
Figure 4-26: LQG Estimated Impulse Response X-Axis Motor to X-Axis PSD ...... 92
Figure 4-27: LQG Estimated Impulse Response X-Axis Motor to Y-Axis PSD ...... 92
Figure 4-28: LQG Estimated Impulse Response Y-Axis Motor to X-Axis PSD ...... 93
Figure 4-29: LQG Estimated Impulse Response Y-Axis Motor to Y-Axis PSD ...... 93
Figure 4-30: Controller Simulation Using FRD Model ...... 94
Figure 4-31: Controller Simulation Using Flexlab Disturbance Data (Y-axis) ...... 96
Figure 4-32: Controller Simulation Using Flexlab Disturbance Data (X-axis) ...... 97
Figure 4-33: Flexlab Demonstration, Y-Axis PSD ...... 99
Figure 4-34: Flexlab Demonstration, X-Axis PSD ...... 100
Figure 4-35: Flexlab Demonstration, Y-Axis Using Thomas’ Settings ...... 101
Figure 4-36: Flexlab Demonstration, X-Axis Using Thomas’ Settings ...... 102
Figure 4-37: Flexlab Demonstration, Y-Axis Using More Conservative Settings ...... 102
Figure 4-38: Flexlab Demonstration, X-Axis Using More Conservative Settings ...... 103
Figure 4-39: Flexlab Demonstration, Y-Axis Using Balanced Settings ...... 103
Figure 4-40: Flexlab Demonstration, X-Axis Using Balanced Settings ...... 104
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LIST OF TABLES Page
Table 4-1: Controllers Attempted ...... 101
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CHAPTER 1: INTRODUCTION
Large flexible structures are found in many applications such as space missions, architectural design, and aircraft manufacturing. One issue when incorporating flexible structures in such a system is the interaction between a control system and a structure’s dynamics. This interaction can significantly degrade system performance due to mismodeling, especially of flexible modes. To accommodate inaccurate models of flexible modes, the control system bandwidth can be limited, but at the expense of performance. To facilitate controller designs capable of desired performance, accurate linear models that include troublesome modes are required. For large flexible structures, system identification (ID), i.e. modeling a system from input/output data, is frequently used to develop more accurate models. Associated with the growing interest in large flexible structures, is the importance of improving methods in this field.
The underlying goal of this research is to produce and illustrate a system ID technique for computing accurate models of the lowest order possible to facilitate controller design using modern techniques to achieve system performance requirements.
To achieve this goal, a hybrid system ID method is developed that employs both a frequency domain system ID method as well as state space system ID method.
1.1 Background
Applications in which a space vehicle has a deployment size larger than the associated launch vehicle are often accomplished with deployable flexible space structural components, such as solar panels, high accuracy sensor placement booms and gravity gradient attitude control booms. Recent developments incorporating flexible components include the United States Air Force Research Laboratory’s (AFRL)
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Deployable Optical Telescope test bed (Schrader, Fetner, Griffin, & Erwin, 2002), the
AFRL Deployable Structures Experiment satellite concept (Adler, et al., April 19-22,
2004), and the deployable elastic composite shape memory alloy reinforced material developed by AFRL and CSA Engineering (Pollard, Murphey, & Sanford, April 23-26,
2007). Understanding the characteristics of such components is therefore of primary importance in advancing development and potential applications (Bihl, Pham, &
Murphey, May 3, 2007).
Additional space-based developments include developments by the National
Aeronautics and Space Administration (NASA) and the Jet Propulsion Laboratory. Both organizations have a long history of incorporating flexible structures in their space vehicle designs. These mission applications include using flexible deployable structures to place instruments as far as possible from the radioisotope thermoelectric generators on the Voyager probes of the 1970s to avoid interference (French & Griffin, 1991), and the application for shuttle missions to test the structural and electrical performance of the large flexible solar panel deployed from a Space Shuttle’s cargo bay (Pappa, Woods-
Vedeler, & Jones, December, 2001). NASA experiments into control-structure
interactions have also included experiments such as the Mini-MAST flexible structures
ground experiments (NASA Controls-Structures Interaction Program, PHASE I Guest
Investigator Program, 1991) and the middeck active control flight experiment (Miller, et
al., 1998).
Aerodynamic research involving control-structures interaction includes projects
such as the joint NASA, AFRL, and Boeing Phantom Works’ Active Aeroelastic Wing
14 research program, which integrates flexible structural behavior of aircraft components and active control systems to replace the function of an aircraft’s control surfaces
(Pendleton, Flick, Paul, Voracek, Reichenbach, & Griffin, April 23-26, 2007). Other applications of research in this field can include hypersonic vehicles, such as the X-43
Hypersonic Scramjet Vehicle, which requires detailed structural knowledge since the engine and airframe are completely integrated where oscillations in the airframe can directly impact engine performance (Adami, Zhu, Bolender, Doman, & Oppenheimer,
August 21-24, 2006).
Flexible structure analysis is not limited to aerospace applications. Structures such as skyscrapers and cellular phone towers often pose many structural challenges such as the effects of wind-induced vibrations, which in severe circumstances can damage or destroy a structure (Wind Control in Building Design, February, 2004). Counteracting these effects is possible with various active and passive damping methods (Wind Control in Building Design, February, 2004). Properly handling these vibrations requires an accurate model to design a controller that can damp vibrations. System ID takes into account the inputs and outputs to a system and is therefore often the best viable means for developing a sufficient model for a specific structure and its surrounding environment.
Technology testing for controller model development and design for flexible structures is essential. In Ohio University’s School of Electrical Engineering and
Computer Science is Flexlab, a flexible structures test bed that was designed to allow for research on flexible structure modeling and control system design methodologies (Blinn,
March, 1997). Primarily, this research has encompassed development and refinement of
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system ID methods and the demonstration of controller designs using models from
system ID.
System ID methods use experimental data to develop mathematical models of
dynamical systems. Of primary interest herein is the development of control-oriented
system ID methods that produce models that include the dynamics of the system plus the dynamics of the sensors and actuators. Such models can then be used for controller
development and refinement (Juang, 1994). Flexlab enables advancements in this field
by offering an easy to use, reconfigurable system that can demonstrate system ID and vibration suppression using active controllers designed from models developed via
system ID.
1.2 Flexlab Testbed Description
Flexlab, Figure 1-1, was designed by Blinn (Blinn, March, 1997) to be a flexible
controls-structure interaction test bed. Flexlab consists of a central structure of a 12 foot
flexible aluminum rod 3/8 inches in diameter, suspended from the ceiling by a two-
degree of freedom gimbaled assembly with direct current (DC) motors orthogonal to each
axis (Blinn, March, 1997). The drive shafts are aligned orthogonally to each other
(Blinn, March, 1997). Therefore, two degree-of-freedom motion about the z-axis
(vertical axis) is permitted, as shown in Figure 1-1 (Blinn, March, 1997).
Flexlab’s motors are employed for both disturbance and control inputs; motion of the structure is measured by sensors mounted on the rod at strategic locations and by an optical position sensing device (PSD) (Blinn, March, 1997). The PSD is mounted on the floor below the bottom of the rod (see Figure 1-1). Two types of sensors are used,
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multiple Kistler K-Beam accelerometers and one Hamamatsu S1200 PSD with a wide-
angle lens. The floor mounted PSD is aligned to a Hamamatsu L2791 highly directive
infrared LED located on the bottom of the structure as shown in Figure 1-1. This device
measures the x and y axis deflection of the bottom of the structure. The accelerometers
are mounted at various points along the structure and aligned to measure accelerations in
both the x and y axes.
Figure 1-1: Current Configuration of Flexlab (Strahler, March, 2000)
Flexlab was designed to be reconfigurable for various types of experiments. To achieve this, masses placed along the structure of Flexlab can be repositioned for various
17 experiments in order to modify the dynamics of the structure (Blinn, March, 1997). In addition, Flexlab was designed with a mounting point for additional actuators placed at the bottom of the structure. This permits control or disturbance actuators, such as cold gas thrusters, to be mounted on the structure but have a minimum effect on its symmetry
(Blinn, March, 1997). Currently, Flexlab is configured for 5 control signal channels (2 of which are for the DC motors) and 8 sensor channels (2 of which are for the PSD).
Figure 1-2: Operational View of Flexlab
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The current configuration of Flexlab, graphically illustrated in Figure 1-1, is pictured in Figure 1-2, and includes a steering mirror mounted along the y-axis. In
previous research this was implemented to demonstrate active optical systems (Strahler,
March, 2000). Although this research has been completed, these components remain on the structure and as a consequence affect structural symmetry, introducing a torsional mode in the x-z plan. This torsional mode is measurable through the use of accelerometers at the end of the steering mirror assembly, which can record the motion of that structure in the x-y plane (Strahler, March, 2000).
Flexlab is operated with a Pentium II 333Mhz computer that has National
Instruments data acquisition cards which handle all system inputs and outputs using
National Instruments’ LabVIEW software (Saunders, November, 2006). Operating
Flexlab is enabled through a LabVIEW based graphical user interface depicted in Figure
1-3. Data collected through the LabVIEW interface can be analyzed with MatLab or similar analytical software (Saunders, November, 2006).
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Figure 1-3: Screenshot of LabVIEW Based Flexlab Control Panel
The benefits of the LabVIEW interface is that the operation of Flexlab can be done with a small learning curve, where previously Flexlab operation was reliant on application specific C and Java programs (Saunders, November, 2006). LabVIEW greatly facilitates the experimentation process with the ability to transfer data and knowledge to novice operators.
1.3 System Identification
Typically there are three methods primarily used to develop linear mathematical models of dynamical systems for controller development, (1) using kinematics and dynamics methods for analysis, (2) finite element analysis, and (3) system ID. Dynamics and kinematics methods are employed by modeling the known forces and moments acting upon a structure, then using this model to predict the behavior of the actual system
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(Fierro & Lewis, 1997), (Tarokh & McDermott, 2005). This is a well known method of
modeling a system and its study forms the basis of dynamics and kinematics theory;
however, it is susceptible to inaccuracies from mismodeling and unanticipated conditions
(Fierro & Lewis, 1997). Its use is typically found in anticipating behavior of a dynamical system during preliminary design stages.
Finite element analysis is a numerical analysis method that models a structure by assembling individual structural elements connected together at various points, called
nodes. The material properties of each element are considered and a system of nodes, called a mesh, is used to simulate the structure (Juang, 1994), (Widas, 1997). However, a model developed with finite element analysis typically experiences significant initial
inaccuracies (due to errors from approximations, mismodeling, and incorrect
assumptions) and typically incorporates refining the model using experimental data
(Juang, 1994). For these reasons finite element analysis methods have not been
considered in conjunction with Flexlab.
System ID develops a mathematical model of a system based upon input/output
data (Ljung, Perspectives on system identification, 2010), with a heavy burden typically
placed on proper sensor placement (Thomas, November, 2006), input signal design
(Pintelon & Schoukens, 2001), data collection that includes significant cycles of the lowest mode (Medina, November, 1991) and appropriate/acceptable estimated model order (Ljung, Perspectives on system identification, 2010) (Markovsky, Willems, van
Huffel, de Moor, & Pintelon, 2005), (Pati, Rezaiifar, Krishnaprasad, & Dayawansa,
1993), (Wahlberg, 1986), (Gallivan & Murray, 2003). Generic system ID processes can
21 be broken into creating an analytical model of a system, estimating the system’s modal and excitation characteristics, determining sensor and actuator locations and requirements, exciting the system to collect experimental data, using a system ID method
(such as mentioned above) on the data to create a model, and then refining the analytical model based on experimental results (Juang, 1994).
Of interest here is developing models that represent the entire system in question, including actuators and sensors, and herein this will be termed “system-ID for control- system design” (Mitchell & Irwin, 2008). This approach is relevant to many situations where ideal sensor and actuator locations are not known a priori or it would be impossible or inefficient to strategically locate sensors and actuators on a given structure, e.g. space vehicle applications where servicing is cost prohibitive or technically impossible.
With experimentally obtained input and output data, system ID methodologies are used to develop mathematical models of a dynamical system plants. Frequency domain system ID methods typically apply various types of frequency domain least squares transfer function analysis for a single-input, multiple-output (SIMO) systems (Juang,
1994), (Thomas, November, 2006). Time domain system ID methods typically also extend least squares system ID (for single-input, single-output systems (SISO)) or state space system ID methods, for SISO, SIMO or multiple-input, multiple-output (MIMO) systems (Juang, 1994), (Thomas, November, 2006).
Understanding the characteristics of the sensors and actuators to be used is also important in system ID. Certain systems can only accept a small range of input signals
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due to the types of actuators being used; understanding the performance characteristics of
sensors being used is equally important or aliasing issues maybe unknowingly be present in the results as a consequence of collecting data discretely (Holst, 1998). Despite the best precautions and system isolation, sensor noise, measurement noise and external interferences will be present in any physical system. System ID research has had to routinely focus on additional considerations to accommodate the extraction of models from noisy data (Juang, 1994), (Thomas, November, 2006), (Fujimori, Nikiforuk, &
Koda, 1995) with estimated model order reduction being an area of ongoing research interest (Ljung, Perspectives on system identification, 2010), (Fujimori, Nikiforuk, &
Koda, 1995). Thomas (Thomas, November, 2006) extended a SISO frequency domain system ID technique termed “Transfer Function Determination Code” (TFDC) using a threshold of the coherence function to eliminate data that was highly corrupted by noise or disturbance in order to improve the fidelity of the models developed using TFDC.
The work presented herein will create a hybrid system ID method extending
TFDC, Thomas’ Coherence Thresholding technique (Thomas, November, 2006), along with a state space system ID method, the Eigensystem Realization Algorithm (ERA).
This thesis will apply the Coherence Threshold to discard noisy frequency domain points from which TFDC will calculate SIMO transfer function models. These models will then be used to produce the needed data to compute MIMO state space models through the
ERA approach from the impulse responses of the SIMO systems. This hybrid method will be illustrated with a contrived example and then with laboratory data from Flexlab.
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1.4 Organization
The following chapters are organized such that Chapter 2 is a literature review of the fundamentals of presently employed System ID techniques. Chapter 3 provides an explanation of the methodology of the hybrid System ID method developed herein, along with a simulation example and subsequent results. System ID using the hybrid method is applied to Flexlab to develop a MIMO model. In Chapter 4 the model is used to design a controller to suppress vibrations and then the design is tested using Flexlab. Chapter 5 concludes this thesis with a discussion of the methods used, results and suggestions for future research.
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CHAPTER 2: SYSTEM IDENTIFICATION METHODS
System ID uses input and output data in conjunction with estimation algorithms to
compute a mathematical relationship between the two sets of data. There are several
techniques used to analyze experimental data for system ID, including time and
frequency domain approaches. In this thesis, a hybrid system ID method will be
developed to create a minimum-order state-space estimated model. To lay the
groundwork for these developments, the three major techniques for performing system ID
are described below. These methods are grouped under the two categories, least squares
and state space.
Least squares system ID extends from ordinary and weighted least squares curve
fitting. System ID methods based upon this approach include: 1) the time domain least
squares system ID technique and 2) the frequency domain least squares, specifically
TFDC. The state space method considered for system ID is the Eigensystem Realization
Algorithm (ERA). This method is an extension of the Ho-Kalman algorithm, which created the first minimal state-space realization from noise-free impulse response data
(Ljung, System Identification Theory for the User, 1999), (Gevers, 2006). Similar to the
Ho-Kalman algorithm, ERA uses impulse response data to generate a state space system model. An interesting aspect of this approach is the ability to develop models from noisy
data, making it an attractive method for modeling dynamical systems where noise-free
data is impossible to gather with physical sensors. Estimated impulse responses are
generated for use with experimental data, as actual impulse responses are impossible to
generate.
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2.1 Ordinary Least Squares
Both time domain least squares system ID and the frequency domain TFDC
methods are built upon least squares curve fitting. A brief explanation of these
underlying principles follows for a better understanding of the proposed solution concept.
Least squares estimation is an estimation technique used in many applications, with one
of the most important being data or curve fitting. It is used to obtain a curve that fits a set
of data points in the best least squares or weighted least squares sense (Maybeck, 1979).
Ordinary least squares can simply be viewed as weighted least squares with unity
weighting. This is important to note, as some least squares based system ID techniques
permit selective weighting of data points to improve the fidelity of the fit by
deemphasizing certain data points, usually data known to be corrupted with noise.
A set of measurement models can be represented as,