Modeling and real-time feedback control of MEMS device

Wang, Limin

doi:10.33915/etd.2156

Cited by 4 publications

(7 citation statements)

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“…Some of these steps can be controlled accurately. Table I shows the resulted parameters of two MEMS microcomb resonators fabricated in the same process [10]. To accurately select MEMS devices, their parameters were identified through genetic algorithm and curve fitting [10].…”

Section: B Experimental Resultsmentioning

confidence: 99%

“…Consider the steady-state Kalman filter model of an LCR associated with different types of fault conditions denoted by subscript as (5) where is the Kalman filter model's state-space variables (displacement and speed of the shuttle), is the system matrix, is the input matrix, is the input vector, is the Kalman filter model's input noise matrix, is the input noise with zero mean and variance of (6) is the measurement vector (displacement and speed of the shuttle), is the output matrix, is the output measurement noise, independent from , with zero mean value as (7) Kalman filter model representation of a system is (8) where is the estimation of state space variable, is the actual output expected from the model, and is the Kalman filter gain recursively obtained through the following procedure: for (9) where is the covariance matrix updated by (10) The covariance matrix updates the Kalman gains recursively. The residual signal is defined as the difference between the output of the Kalman filter model and that of the actual operating system.…”

Section: A Kalman Filter Design For Lcrsmentioning

confidence: 99%

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Fault Diagnosis of MEMS Lateral Comb Resonators Using Multiple-Model Adaptive Estimators

Izadian

Famouri

2010

IEEE Trans. Contr. Syst. Technol.

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In this brief a fault diagnostic unit is developed for microelectromechanical systems (MEMS) by means of multiple model adaptive estimation technique. Fault modeling tools such as contamination and reliability analysis of microelectromechanical layout enabled interpretation of microsystems behavior by evaluating their structural variations and modeling them in form of electric circuits. This technique cannot directly diagnose the faults during operation of microsystems. However, these fault-representing models can be used in multiple model adaptive estimation technique to form fault diagnosis units. Here, fault-representing systems are modeled by Kalman filters in real-time applications and are used to evaluate the fault in microsystems. MEMS lateral comb resonators are fabricated to experimentally demonstrate the fault diagnosis performance in multiple model adaptive estimation technique.

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Section: B Experimental Resultsmentioning

confidence: 99%

Section: A Kalman Filter Design For Lcrsmentioning

confidence: 99%

Fault Diagnosis of MEMS Lateral Comb Resonators Using Multiple-Model Adaptive Estimators

Izadian

Famouri

2010

IEEE Trans. Contr. Syst. Technol.

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“…The light intensity gives a peak and valley when passing through an opening and space of the grating, thus giving information about the total displacement of the system. An autorecovery control block [38], is used to recover the displacement of the shuttle from this optical data. An optical system enables a completely decoupled measurement from the electrostatic drive [19], giving accurate results.…”

Section: Measurement Techniquesmentioning

confidence: 99%

Optical detection of multiple faults of a MEMS based linear comb resonator

Konduparthi¹

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OPTICAL DETECTION OF MULTIPLE FAULTS ON A MEMS BASEDLINEAR COMB RESONATOR LAKSHMI DEEPASREE KONDUPARTHI Investigating the dynamic behavior of any Micro Electro Mechanical system is critical for better control and fault prediction. Linear Comb Resonator is one such MEMS generic device that I would like to thank my committee members, Dr. Larry Hornak, Dr. Parviz Famouri, and Dr. Dimitris Korakakis. I would like to specially thank Dr.Hornak, my research advisor, for his guidance, patience and support throughout the project. I would also like to thank Dr. Nilay Mukherjee for guiding me through the validation process. I thank Dr.Kolin Brown for his training and constant inputs. I also thank NASA Epscor for funding this project Additionally, I thank members of MEMS research group Afshin Izadian, Limin Wang, William McCormick and Scott Rittenhouse for their help and training in understanding the project. I also would like to thank my colleagues in MSRC for their encouragement. Above all, I thank my parents for motivating and inspiring me to pursue an advanced degree. I also thank my sister for her love and encouragement. In addition I thank my friends and roomates. iv Contents Dedication iii Abstract ii

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“…The laser beam can pass through the openings on the shuttle of MEMS and is blocked elsewhere, which produces pulses that contain the displacement information. The optical encoded waveform requires a data recovery unit designed by (Wang, 2005) to extract the location of the shuttle instantaneously. The through wafer optical monitoring system consists of a laser wave-guide emitted from a laser diode.…”

Section: Optical Displacement Monitoringmentioning

confidence: 99%

“…88 2000), (Park, Horowitz & Tan. 2001), ( Wang. 2005), , , for optical displacement monitoring technique.…”

Section: )mentioning

confidence: 99%

Automatic Control and Fault Diagnosis of MEMS Lateral Comb Resonators

Izadian¹

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Automatic Control and Fault Diagnosis of MEMS Lateral Comb Resonators Afshin Izadian Recent advancements in microfabrication of Micro Electro Mechanical Systems have made MEMS an important part of many applications such as safety and sensor/control devices. Miniature structure of MEMS makes them very sensitive to the environmental and operating conditions. In addition, fault in the device might change the parameters and result in unwanted behavioral variations. Therefore, imperfect device structure, fault and operating point dependencies suggest for active control of MEMS. This research is focused on two main areas of control and fault diagnosis of MEMS devices. In the control part, the application of adaptive controllers is introduced for trajectory control of the device under health and fault conditions. Fault in different forms in the structure of the device are modeled and foundry manufactured for experimental verifications. Pull-in voltage effect in the MEMS Lateral Comb Resonators are investigated and controlled by variable structure controllers. Reliability of operation is enhanced by active control of the device under fault conditions. The second part of this research is focused on the fault diagnosis of the MEMS devices. Fault is introduced and investigated for better understanding of the system behavioral changes. Modeling of the device in different operating conditions suggests for the multiple-model adaptive estimation (MMAE) fault diagnosis technique. Application of Kalman filters in MMAE is investigated and the performance of the fault diagnosis is compared with other techniques such as self-tuning and auto self-tuning techniques. According to the varying parameters of the system, online parameter identification systems are required to monitor the parameter variations and model the system accurately. Self-tuning banks are applied and combined with MMAE to provide accurate fault diagnosis systems. Different parameter identification techniques result in different system performances. In this regard, this research investigates the application of Recursive Least Square with Forgetting Factor. Different techniques for tuning of forgetting factor value are introduced and their results are compared for better performance. The organization of this dissertation is as follows: Chapter I introduces the structure of the MEMS Lateral Comb Resonator; Chapter II introduces the application of control techniques and displacement feedback approach. Chapter III investigates the control approach and experimental results. In chapter IV, a new controller is introduced and designed for the MEMS trajectory controls. Chapter V is about the fault and different techniques of fault diagnosis in MEMS LCRs. Chapter 6 is the future work suggested through the current results and observations. Each chapter contains a section to summarize the concluding remarks. iii To my Parents and my wife Pardis iv Acknowledgement: I would like to thank all previous members of WVU MEMS research group, whose work has made this research possible. E...

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Modeling and real-time feedback control of MEMS device

Cited by 4 publications

References 38 publications

Fault Diagnosis of MEMS Lateral Comb Resonators Using Multiple-Model Adaptive Estimators

Fault Diagnosis of MEMS Lateral Comb Resonators Using Multiple-Model Adaptive Estimators

Optical detection of multiple faults of a MEMS based linear comb resonator

Automatic Control and Fault Diagnosis of MEMS Lateral Comb Resonators

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