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.

/pdf/automatic-control-and-fault-diagnosis-of-mems-lateral-comb-47cuc7gy31.pdf

Automatic Control and Fault Diagnosis of MEMS Lateral Comb Resonators

The literature review shows that there are not many examples of adaptive systems synthesis algorithms. The adaptive block is usually presented in the given structures but information of basic principles and adaptive algorithm implemented behind is hardly provided. The present material proposes a practical solution with accent to the synthesis of the adaptive structure of the modal state controller. The algorithms used for adaptive observation and modal control are preliminary set and developed. Auxiliary pulse-relay controller (PRC) is implemented for control purposes during the observation time, which control signal is not only dependent on the control error. It is programmed to ensure that for n steps, if the control signal $u(k)$ has been set to the lower/or higher level, it will switch for 1 step to the alternate level. This solution will guarantee sufficiently informative signal feed to the object input.

Single Input Single Output System with Modal Adaptive State Controller

Emerging smart technologies add elements of intelligence, cooperation, and adaptivity to physical entities, enabling them to interact with each other and with humans as systems of (human-)cyber-physical systems or (H)CPSes. Hybrid automata, in their various flavours, have been suggested as a formal model accurately capturing CPS dynamics and thus facilitating exhaustive behavioural analysis of interacting CPSes with mathematical rigour.

Guess What I’m Doing!

Most of the industrial and chemical processes are raultivariable in nature. There are always complicated interactions existing between the measurement signals and control signals. Because of the interactions among the input and output variables, it is difficult to design suitable controller for Multi Input Multi Output (MEMO) systems. In control engineering, the goal of any controller design is to stabilize the system to track the set point and to reject the load disturbances in spite of the control challenges such as non-minimum phase behaviour, model uncertainties, unmeasured disturbances and constraints on input and output variables. In this paper, a model based controller is designed for a nonlinear multivariable process with a transmission zero to enhance its performance. To accomplish this goal, the experimental multivariable system, used in various control literature, called Quadruple Tank System (QTS) is taken for the controller synthesis. Internal Model Control (IMC) concept is used for the model based multi loop control for QTS and its performance is analyzed through simulation using MATLAB/SIMULENK. Simulation results proved the robustness of the proposed controller against the conventional controller under control challenges.

Design of model based controller to improve the performance of a nonlinear multivariable system with control challenges

This project exploits subspace prediction methods in order to apply a novel direct "predictive" control design technique, which can be viewed as an extension of model free subspace based linear quadratic Gaussian (LQG) control, and in the class of adaptive control. The main purpose of this study is to design a much simpler control approach for a wastewater treatment plant using a data driven direct adaptive predictive controller based on subspace identification of prediction matrices. The general direct control design problem requires the engineer to collect experimental data, and choose a performance objective. With these design choices, it is then possible to calculate a control law that optimizes expected future performance. Recently, there has been significant interest in developing a direct control design methodology producing a more reliable and automated control design technique. Effective control of wastewater treatment plants (WWTPs) has been receiving rising attention during the last decade due to increasing concern about environmental issues. In this sense, the importance of studies concentrating on control and simulation of WWTP is remaining intact. Activated sludge process is commonly used in biological wastewater treatment. The applied methodology of this project is supposed to regulate the substrate concentration and dissolved oxygen concentration to specified values, compensate the disturbances may occur in the load influents as well as to track any mechanistic or kinetic parameter variation immediately in the shortest possible time. Hence, direct adaptive predictive control (DAMPC) can provide simplicity, good performance and stability robustness of an activated sludge process.

Subspace based direct adapitve control for a class of nonlinear systems

The field of indirect adaptive control has been intensively studied for simple-input/simple output (SISO) system as well as multivariable (MIMO) systems based on I/O models. State-space approaches for SISO system can also be found in the literature. Although the state-space representation is a very convenient description for multivariable systems, only few times it was applied to design indirect adaptive control systems. In this paper, these contributions are reviewed and analysed including advantages and drawbacks.

http://ieeexplore.ieee.org/iel5/9768/30803/01425956.pdf

Multivariable adaptive state-space control: A survey

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

A. Gambler

Author Tools

Chat about Author

Papers

Multivariable adaptive state-space control: A survey