Zugl.: München, Techn. Univ., Diss., 2007

Zugl.: Hannover, Univ., Diss., 2008

Zugl.: Hannover, University, Diss., 2009.

Zugl.: München, Techn. Univ., Diss., 2009.

Zugl.: Braunschweig, Techn. Univ., Diss., 2009

Thesis--Universität Ulm, 2006. / Includes bibliographical references.

This work is inspired by the peculiar behavior of the natural systems, namely the ability to produce self-sustained oscillations in the level of tens of Hertz in constant ambient conditions. This feature is one of the key signatures prescribed to living organisms. The firing rate of neuronal cells, a pulsating heart, or the beating of cilia and flagella are among many biological examples that possess amazing functionalities and unprecedented intelligence solely relying on bio-electro-chemical processes. Exploring shapeable polymeric technologies, new self-oscillating artificial microsystems were developed within this thesis. These microsystems rely on the novel nonlinear architecture that exhibits a negative differential resistance (NDR) within the parametric response that enables periodic oscillations. These systems are made of polymers and metals and were microfabricated in a planar fashion. The electrochemically deposited ionic electroactive polymers act as actuators of the system. Upon the self-assembly process, due to the interlayer strains, the planar device transforms into a three-dimensional soft nonlinear system that is able to perform self-sustained relaxation oscillations when subjected to a constant electric field while consuming extremely low powers (as low as several microwatts). The parameters of these systems were tuned for a high oscillation amplitude and frequency. This electro-mechanical parametric relaxation oscillator (EMPRO) can generate a rhythmic motion at stroke frequencies that are biologically relevant reaching up to ~95 Hz. The EMPRO oscillations at high frequencies generate a flow in the surrounding liquid, which was observed in the form of vortices around the micro actuators. This flow was further studied in ex-vivo conditions by measuring Doppler shifts of ultrasound waves. The EMPRO was made autonomous by integrating an electrochemical voltaic cell. Four different electrochemical batteries were tested to match the power consumption of the EMPRO system and electrochemical compatibility of the surrounding media. An Ag-Mg primary cell was then integrated with the EMPRO for autonomous operation without the need for external power sources, cables or controllers. This biomimicking self-powered self-sustaining oscillating microsystem is envisioned to be useful in novel application scenarios operating at low Reynolds numbers in biologically relevant conditions. Furthermore, as the system is electromechanical in nature, it could be integrated with electronic components such as sensors and communication devices in the next generation of autonomous microsystems.:  Table of contents Acronyms 7 1 Introduction 8 1.1 Motivation 9 1.2 Objectives 9 1.3 Thesis organization 10 2 Background 12 2.1 A brief review on nonlinear self-oscillation 12 2.2 Self-oscillating biological systems 13 2.3 Stimuli responsive materials 15 2.3.1 Electroactive polymers in electrochemical cells 16 2.3.2 Sources of electrical field for electroactive polymers 24 2.4 Self-oscillating synthetic systems 27 2.5 Movement in low Reynolds number regime 33 3 Materials and methods 38 3.1 Deposition methods 38 3.1.1 Photolithography 38 3.1.2 Plasma sputtering 41 3.1.3 Atomic layer deposition 42 3.1.4 Electrochemical polymerization 44 3.2 Shapeable polymeric platform technology 46 3.2.1 Sacrificial layer 46 3.2.2 Hydrogel swelling layer 47 3.2.3 Polyimide reinforcing layer 48 3.3 Characterization methods 49 3.3.1 Profilometry 49 3.3.2 Scanning electron and focused ion beam microscopy 50 3.3.3 Cyclic Voltammetry 52 3.3.4 Ultrasound and Doppler shift measurements 53 4 Electromechanical Parametric Relaxation Oscillators (EMPROs) 56 4.1 Relaxation oscillation in EMPROs 56 4.2 Theory of EMPRO relaxation oscillations 61 4.3 Realization of EMPROs 67 4.3.1 Design parameters of EMPROs 67 4.3.2 EMPRO on-chip battery integration 71 4.4 Fabrication of autonomous EMPROs 76 5 EMPRO performances 84 5.1 Externally biased EMPROs 84 5.2 Autonomous EMPROs 95 6 Conclusions and outlook 98 6.1 Outlook 99 Bibliography i List of Figures and Tables xi Versicherung xiii Acknowledgements xiv Scientific publications and contributions xvi Theses xvii Curriculum Vitae xix

Prognostiziert durch Konzepte der Industrie 4.0 und den Cyber-Physischen-Systemen, können autonome Systeme zukünftig dynamisch auf Datenquellen in ihrer Umgebung zugreifen. Während die gemeinsame Nutzung solcher Datenquellen ein enormes Performanzpotenzial bietet, stellt die benötigte Systemarchitektur vorherrschende Sicherheitsprozesse vor neue Herausforderungen. Die vorliegende Arbeit motiviert zunächst, dass diese nur zur Laufzeit des Systems adressiert werden könne, bevor sie daraus zwei zentrale Ziele ableitet und verfolgt. Zum einen wird ein Beschreibungsmodel für die Darstellung von Fehlercharakteristika gemeinsam genutzter Daten vorgestellt. Dieses generische Fehlermodell erlaubt es zum anderen eine Sicherheitsanalyse zu definieren, die eine spezifische, dynamische Systemkomposition zur Laufzeit mit Hinblick auf die zu erwartenden Unsicherheiten bewerten kann. Die als Region of Safety betitelte Analysestrategie erlaubt, in Kombination mit dem generischen Fehlermodell, die Sicherheit der auf gemeinsam genutzten Daten basierenden Kollisionsvermeidungsstrategie zweier Roboter noch zur Designzeit zu garantieren, obwohl die spezifischen Fehlercharakteristika der Daten erst zur Laufzeit bekannt werden.:List of Acronyms List of Theorems List of Definitions List of Figures List of Tables 1. Introduction – Safety in Future Smart Industries 1.1. The Example of Smart Warehouses 1.2. Functional Safety Standards 1.2.1. Overview of Functional Safety Standards 1.2.2. IEC 61508 1.3. Scope of this Thesis 1.3.1. Objectives 1.3.2. Contributions 1.3.3. Outline 1.4. Related Publications by the Author 1.5. Mathematical Notation 2. State of the Art 2.1. State of the Art in Run-Time Safety Assessment 2.1.1. Approaches at the Functional Level 2.1.2. Approaches at the Technical Level 2.1.3. Conclusions 2.2. State of the Art in Failure Modeling 2.2.1. The Definition of (Sensor) Failure Model 2.2.2. Interval-Based Failure Modeling 2.2.3. Distribution-Based Failure Modeling 2.2.4. Failure-Type-Based Failure Modeling 2.2.5. Conclusions 2.3. Conclusions from the State of the Art 3. Generic Failure Model 3.1. Defining the Generic Failure Model 3.1.1. Time- and Value-Correlated Random Distribution 3.1.2. A Failure Type’s Failure Amplitudes 3.1.3. A Failure Type’s State Function 3.1.4. Polynomial Representation of a Failure Type 3.1.5. Discussion on the Fulfillment of the Predefined Criteria 3.2. Converting a Generic Failure Model to an Interval 3.2.1. Converting a Time- and Value-Correlated Random Distribution 3.2.2. A Failure Type’s Interval 3.3. Processing Chain for Generating Generic Failure Models 3.3.1. Identifying Failure Types 3.3.2. Parameterizing Failure Types 3.3.3. Confidence Calculation 3.4. Exemplary Application to Artificial Failure Characteristics 3.4.1. Generating the Artificial Data Set – Manually Designing GFMs 3.4.2. Identifying Failure Types 3.4.3. Parameterizing Failure Types 3.4.4. Confidence Calculation 3.4.5. Comparison to State-of-the-Art Models 3.5. Summary 4. Region of Safety 4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems 4.2. Regions of Safety for Dynamically Composed Systems 4.2.1. Estimating Regions of Attraction in Presence of Uncertainty 4.2.2. Introducing the Concept of Region of Safety 4.2.3. Discussion on the Fulfillment of the Predefined Criteria 4.3. Evaluating the Concept of Region of Safety 4.3.1. Defining the Scenario and Considered Uncertainties 4.3.2. Designing a Control Lyapunov Function 4.3.3. Determining an Appropriate Value for λc 4.3.4. The Effect of Varying Sensor Failures on Regions of Safety 4.4. Summary 5. Evaluation and Integration 5.1. Multi-Robot Collision Avoidance 5.1.1. Assumptions 5.1.2. Design of the Circle and Navigation Scenarios 5.1.3. Kinematics 5.1.4. Control Policy 5.1.5. Intention Modeling by Model Uncertainty 5.1.6. Fusing Regions of Safety of Multiple Stability Points 5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model 5.2.1. Data Acquisition 5.2.2. Failure Model Generation 5.2.3. Evaluating the Quality of the Failure Model 5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy 5.3.1. Configuration for Region of Safety Estimation 5.3.2. Estimating Regions of Safety 5.3.3. Evaluation Using the Circle Scenario 5.3.4. Evaluation Using the Navigation Scenario 5.4. Summary 6. Conclusions and Future Work 6.1. Summary 6.2. Limitations and Future Work 6.2.1. Limitations and Future Work on the Generic Failure Model 6.2.2. Limitations and Future Work on Region of Safety 6.2.3. Future Work on Safety in Dynamically Composed Systems Appendices A. Defining Factors of Risk According to IEC 61508 B. Evaluation Results for the Identification Stage C. Overview of Failure Amplitudes of Marker Detection Results Bibliography / The concepts of Cyber-Physical-Systems and Industry 4.0 prognosticate autonomous systems to integrate sources of shared data dynamically at their run-time. While this promises substantial increases in their performance, the openness of the required system architecture poses new challenges to processes guaranteeing their safety. This thesis firstly motivates that these can be addressed only at their run-time, before it derives and pursues two corresponding goals. Firstly, a model for describing failure characteristics of shared data is presented. Secondly, this Generic Failure Model is built upon to define a run-time safety assessment methodology that enables analyzing dynamic system compositions integrating shared data with respect to the expected uncertainties at run-time. This analysis strategy, entitled Region of Safety, allows in combination with the generic failure model to guarantee the safety of robots sharing position data for collision avoidance already at design-time, although specific failure characteristics become available only at run-time.:List of Acronyms List of Theorems List of Definitions List of Figures List of Tables 1. Introduction – Safety in Future Smart Industries 1.1. The Example of Smart Warehouses 1.2. Functional Safety Standards 1.2.1. Overview of Functional Safety Standards 1.2.2. IEC 61508 1.3. Scope of this Thesis 1.3.1. Objectives 1.3.2. Contributions 1.3.3. Outline 1.4. Related Publications by the Author 1.5. Mathematical Notation 2. State of the Art 2.1. State of the Art in Run-Time Safety Assessment 2.1.1. Approaches at the Functional Level 2.1.2. Approaches at the Technical Level 2.1.3. Conclusions 2.2. State of the Art in Failure Modeling 2.2.1. The Definition of (Sensor) Failure Model 2.2.2. Interval-Based Failure Modeling 2.2.3. Distribution-Based Failure Modeling 2.2.4. Failure-Type-Based Failure Modeling 2.2.5. Conclusions 2.3. Conclusions from the State of the Art 3. Generic Failure Model 3.1. Defining the Generic Failure Model 3.1.1. Time- and Value-Correlated Random Distribution 3.1.2. A Failure Type’s Failure Amplitudes 3.1.3. A Failure Type’s State Function 3.1.4. Polynomial Representation of a Failure Type 3.1.5. Discussion on the Fulfillment of the Predefined Criteria 3.2. Converting a Generic Failure Model to an Interval 3.2.1. Converting a Time- and Value-Correlated Random Distribution 3.2.2. A Failure Type’s Interval 3.3. Processing Chain for Generating Generic Failure Models 3.3.1. Identifying Failure Types 3.3.2. Parameterizing Failure Types 3.3.3. Confidence Calculation 3.4. Exemplary Application to Artificial Failure Characteristics 3.4.1. Generating the Artificial Data Set – Manually Designing GFMs 3.4.2. Identifying Failure Types 3.4.3. Parameterizing Failure Types 3.4.4. Confidence Calculation 3.4.5. Comparison to State-of-the-Art Models 3.5. Summary 4. Region of Safety 4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems 4.2. Regions of Safety for Dynamically Composed Systems 4.2.1. Estimating Regions of Attraction in Presence of Uncertainty 4.2.2. Introducing the Concept of Region of Safety 4.2.3. Discussion on the Fulfillment of the Predefined Criteria 4.3. Evaluating the Concept of Region of Safety 4.3.1. Defining the Scenario and Considered Uncertainties 4.3.2. Designing a Control Lyapunov Function 4.3.3. Determining an Appropriate Value for λc 4.3.4. The Effect of Varying Sensor Failures on Regions of Safety 4.4. Summary 5. Evaluation and Integration 5.1. Multi-Robot Collision Avoidance 5.1.1. Assumptions 5.1.2. Design of the Circle and Navigation Scenarios 5.1.3. Kinematics 5.1.4. Control Policy 5.1.5. Intention Modeling by Model Uncertainty 5.1.6. Fusing Regions of Safety of Multiple Stability Points 5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model 5.2.1. Data Acquisition 5.2.2. Failure Model Generation 5.2.3. Evaluating the Quality of the Failure Model 5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy 5.3.1. Configuration for Region of Safety Estimation 5.3.2. Estimating Regions of Safety 5.3.3. Evaluation Using the Circle Scenario 5.3.4. Evaluation Using the Navigation Scenario 5.4. Summary 6. Conclusions and Future Work 6.1. Summary 6.2. Limitations and Future Work 6.2.1. Limitations and Future Work on the Generic Failure Model 6.2.2. Limitations and Future Work on Region of Safety 6.2.3. Future Work on Safety in Dynamically Composed Systems Appendices A. Defining Factors of Risk According to IEC 61508 B. Evaluation Results for the Identification Stage C. Overview of Failure Amplitudes of Marker Detection Results Bibliography

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