Device Applications of Nonlinear Dynamics (eBook)

Preface 6
Contents 10
Part I Opening Plenary Talk 14
Use of Chaos to Improve Equipments 15
1 Application of Chaos to Sonars 15
2 Chaos to Improve Motion Control of Microrobots 17
3 Separation and Synchronization of Chaotic Circuits 19
4 Conclusions 20
References 21
Part II Nonlinear Dynamics, Materials and Sensing Devices 23
Invited Papers 25
Noise Induced Switching Between Oscillation States in a Nonlinear Micromechanical Oscillator 27
References 34
Nonadiabaticity in Modulated Optical Traps 37
1 Introduction 37
2 Static Optical Potential 38
3 Periodically-Modulated Optical Potential 41
4 Experimental Results 43
5 Discussion 44
6 Conclusions 45
Acknowledgments 46
References 46
Signal Processing and Control in Nonlinear Nanomechanical Systems 49
1 Introduction 49
2 Nonlinearity in Nanomechanical Structures 50
3 Control by Stochastic Resonance 55
Acknowledgements 61
References 61
Signal Modulation by Martensitic Control of Shape Memory Alloy Thin Film Actuator Architectures1 63
1 Introduction 64
2 Actuation Principles in Shape Memory Alloys Thin Film Composites 66
3 Experimental Details 73
4 Conclusions 75
References 76
Exploiting Dynamic Cooperative Behavior in a Coupled- Core Fluxgate Magnetometer 79
1 Introduction 79
2 Coupled-Core Fluxgate Magnetometers 81
3 Discussion and Concluding Remarks 90
Acknowledgements 93
References 93
Motion Sensors and Actuators Based on Ionic Polymer- Metal Composites 95
1 Introduction 95
2 IPMCs Fundamentals 96
3 IPMCs Modelling 98
Conclusions 106
Acknowledgements 108
References 108
Contributed Papers 113
Pattern Formation Stability and Collapse in 2D Driven Particle Systems 115
1 Introduction 115
2 Stability and Collapse 116
3 The Model 118
4 Pattern Formation 119
5 Variations 121
6 Continuum Limit 122
7 Further Work and Conclusions 124
Acknowledgments 125
References 125
Uncertainty Sources in RTD-Fluxgate 127
1 Introduction 127
2 An Overview of the RTD-Fluxgate 128
3 Uncertainty Sources in RTD-Fluxgate 135
Acknowledgment 138
References 138
Modeling and Design of Ferro.uidic Sensors 141
1 Introduction 141
2 Governing Force and Equations 142
3 Simulation and System Setup 143
4 Experimental Results 143
5 Dynamic Accelerometer Performances 146
6 Resonant Accelerometer 147
7 Conclusion 148
References 148
Thermocromic Materials for Temperature Sensors in New Applications 151
1 Introduction 151
2 Materials 152
3 Processes 153
4 Results 154
5 Conclusions 155
References 155
A SQUID Ring-Resonator Finate State Machine 157
1 Introduction 157
2 Background 158
3 Results 159
4 Conclusion 161
Acknowledgements 162
References 163
Part III Signal Processing and Applications 165
Invited Papers 167
Suprathreshold Stochastic Resonance Mediated by Multiplicative Noise 169
1 Introduction 169
2 Models 170
3 Mutual Information 172
4 Results 173
5 Conclusion 179
References 180
Noise for Health: Phage-Based Rapid Bacterial Identification Method* 183
1 Introduction 183
2 Background 184
3 Experimental Analysis 185
4 Earlier Model Considerations 188
5 Conclusions 189
References 190
Contributed Papers 193
Parametric Resonance Near Hopf-Turing Instability Boundary 195
References 200
Recurrent Neural Networks in Rainfall–Runoff Modeling at Daily Scale 203
1 Introduction 203
2 Problem Description 205
3 Procedure Description 205
4 Network Methodologies 206
5 Analysis of Physical Context 210
6 Comments 211
References 211
Distributed Data Acquisition System for Environment Monitoring Nonlinear Processes 213
1 Introduction 213
2 Why Data Acquisition Through Internet 214
3 Virtual Instrument 216
4 The Communication Module 218
5 The Software Applications 219
6 Conclusions 220
References 221
Automatic Safety Control in Food Processing 223
1 Introduction 223
2 Method Description 223
3 Validation Algorithm 226
4 Numerical Analysis 227
5 Conclusions 227
References 228
Using a TI C6701 DSP Rapid Prototyping System for Nonlinear Adaptive Filtering to Mitigate Interference 229
1 Introduction 229
2 Nonlinear Applications on the Rapid Prototyping System 229
3 Rapid Prototyping System Structure 230
4 Conclusions 232
References 233
Gunn Oscillations Described by the MEP Hydrodynamical Model of Semiconductors 235
1 The Model 235
2 Simulations of Gunn Oscillations 237
Acknowledgments 240
References 240
Dynamic Test Data Generation for the Nonlinear Models with Genetic Algorithms 241
1 Introduction 241
2 Genetic Algorithms 242
3 An Automatic Pilot System 243
References 246
Neuro-Fuzzy Based Nonlinear Models 249
1 Introduction 249
2 The Determination of Membership Functions Paramaters 252
3 Adaptive Fuzzy Conrol Algorithm 253
4 Conclusions 256
References 256
Reconfigurable Pattern Generators Using Nonlinear Electronic Circuits 257
1 Introduction 257
2 Background 258
3 CPG Network 258
4 Patterns and Locomotion 261
5 Conclusions and Future Work 263
References 263
Configuring A Non-Linear Process Control System Using Virtual Instrumentation 265
1 Introduction and History 265
2 Kyoto Protocol 266
3 Mauna Loa 266
4 Carbon Cycle Modelling 267
5 Model Results 269
6 Conclusions 270
References 271

2 Chaos to Improve Motion Control of Microrobots (p. 5-6)

The second application deals with the use of chaos for motion control in microrobotics. In particular, a microrobot actuated by piezoelectric elements, named PLIF (Piezo Light Intelligent Flea) [5], designed to be fast, small, light and cheap, is taken into account and chaos is used to enhance the motion capabilities on irregular surfaces.

Usually, the actuation of robot legs is controlled by square wave signals characterized by a .xed amplitude and a variable switching frequency. In this application these signals are generated performing a frequency modulation driven by the chaotic evolution of Chua’s circuit state variables. The smooth changes of the actuation signal frequency, performed by our chaotic system, enhances the microrobot walking capabilities especially when walking on irregular surfaces. Indeed, when driven with a constant frequency control signal, the microrobot is able to walk on regular surfaces if the frequency is appropriately tuned, but very small irregularities (such as grazes) can be a serious problem for the microrobot. By exploiting the widespread spectrum of a chaotic signal, a control signal with erratically varying frequency is provided to the robot making it able to deal with asperities in the surface and adaptable to di.erent surfaces. In fact, in our microrobot chaos is directly used in the actuation system to modulate the signals devoted to the robot control.

The actuation of the microrobot used in this application is based on piezoelectric ceramic actuators. Piezoelectric materials are particular structures able to produce a voltage when deformed and, viceversa, an excitation voltage induces a deformation that can generate a force. Hence, it is possible to use piezoelectric materials as deformation sensors as well as actuators. The piezoelectric actuator is made up of two piezoceramics joined and isolated through a resin coverage. The two elements are excited alternatively: one of the elements, excited, shortens while the other one stretches making the entire structure bending toward the short side. To recover the original position it is su.cient to reverse the excitation voltage. The piezoelectric actuators are used to build the legs of the robot, each leg is therefore actuated by a .exor-extensor-like pair. The whole structure of the PLIF robot, designed to be light and as small as possible, is shown in Fig. 1(a).

The motion control system generates and controls the locomotion pattern of the microrobot which preliminary experimental tests have been revealed to be the most e.ective for the adopted structure. The motion pattern is characterized by the simultaneous actuation of the two legs. Each robot leg follows this movement sequence: femur raising, tibia moving forward, femur going down, tibia moving backward.

In [5] and related works, the locomotion pattern is realized by an oscillator which generates a square wave signal with constant frequency and by a power circuitry (driver) providing the voltage supply needed by the piezoelectric actuator. In this application, in order to provide the robot with adaptive capabilities, this control scheme has been modi.ed as shown in Fig. 1(b), where chaotic modulation of the control signal is included. In this way, the generation of control signals with time-variant frequency can be accomplished. The frequency of the control signal changes as function of the state variables of a chaotic circuit. Thus, the unpredictable behavior of the chaotic modulating signal is exploited to obtain a control system able to explore at each step new solutions to the motion control problem. In particular, one of the state variables of a Chua’s circuit is used as modulation driving signal.

In order to evaluate the performances of the microrobot, three kind of tests were performed. In the .rst set of tests the microrobot walks on di.erent smooth surfaces like an iron or wooden layer. In the second set of tests the surfaces are grazed in order to compare the performances in terms of speed obtained with or without chaotic modulation. The last set of tests concerns the overloading of the microrobot structure in order to verify if the introduction of chaotically modulated control signals is able to improve the motion also in presence of heavy structures.

Comparative results show that driving the actuation by using chaotic modulation leads to consistent improvements in terms of two factors: robot speed and motion on irregular surfaces. In particular on grazed surfaces, the robot, driven by chaotically modulated signals, is able to pass over the scratches while in the case of constant frequency actuation signal the robot often stops or decreases its velocity. To graphically show the improvements obtained using chaotically modulated frequency signal, the robot has been equipped with a led that lights up when the robot is actuated. A camera with a long exposure time has been used in order to take pictures which traces the robot trajectory. As shown in Fig. 2, improvements are clearly visible simply comparing the trajectory of the red led.