Baglio / Bulsara Device Applications of Nonlinear Dynamics

1. Auflage 2007
ISBN: 978-3-540-33878-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 272 Seiten, eBook

Reihe: Understanding Complex Systems

The past two decades have witnessed an explosion of ideas in the general ?eld of nonlinear dynamics. In fact, it has become increasingly clear that areas as diverse as signal processing, lasers, molecular motors, and biomedical anomalies have a c- mon underlying thread: the dynamics that underpin these systems are inh- ently nonlinear. Yet, while there has been signi?cant progress in the theory of nonlinear phenomena under an assortment of system boundary conditions andpreparations,thereexistcomparativelyfewdevicesthatactuallytakethis rich behavior into account. In the presence of background noise (a given, for most practical appli- tions), the underlying dynamic phenomena become even richer, with the noise actually mediating cooperative behavior that, when properly understood, can lead to signi?cant performance enhancements; a striking example of this - havior occurs, for example, when the underlying dynamics undergoes a bif- cation from static to oscillating behavior when a control parameter is swept through a critical value. If properly understood, theoretically, the (suitably quanti?ed) system response can be signi?cantly enhanced near the onset of the bifurcation. Examples of this behavior have been observed in a large n- ber of laboratory experiments on systems ranging from solid state lasers, to SQUIDs, and such behavior has been hypothesized to account for some of the more striking information processing properties of biological neurons. In many cases, background noise can precipitate this behavior, thereby playing a signi?cant role in the optimization of the response of these systems to small external perturbations.

Zielgruppe

Research

Autoren/Hrsg.

Baglio, Salvatore

Bulsara, Adi

Inhaltsverzeichnis

Opening Plenary Talk.- Use of Chaos to Improve Equipments.- Nonlinear Dynamics, Materials and Sensing Devices.- Noise Induced Switching Between Oscillation States in a Nonlinear Micromechanical Oscillator.- Nonadiabaticity in Modulated Optical Traps.- Signal Processing and Control in Nonlinear Nanomechanical Systems.- Signal Modulation by Martensitic Control of Shape Memory Alloy Thin Film Actuator Architectures.- Exploiting Dynamic Cooperative Behavior in a Coupled-Core Fluxgate Magnetometer.- Motion Sensors and Actuators Based on Ionic Polymer-Metal Composites.- Pattern Formation Stability and Collapse in 2D Driven Particle Systems.- Uncertainty Sources in RTD-Fluxgate.- Modeling and Design of Ferrofluidic Sensors.- Thermocromic Materials for Temperature Sensors in New Applications.- A SQUID Ring-Resonator Finate State Machine.- Signal Processing and Applications.- Suprathreshold Stochastic Resonance Mediated by Multiplicative Noise.- Noise for Health: Phage-Based Rapid Bacterial Identification Method.- Parametric Resonance Near Hopf-Turing Instability Boundary.- Recurrent Neural Networks in Rainfall–Runoff Modeling at Daily Scale.- Distributed Data Acquisition System for Environment Monitoring Nonlinear Processes.- Automatic Safety Control in Food Processing.- Using a TI C6701 DSP Rapid Prototyping System for Nonlinear Adaptive Filtering to Mitigate Interference.- Gunn Oscillations Described by the MEP Hydrodynamical Model of Semiconductors.- Dynamic Test Data Generation for the Nonlinear Models with Genetic Algorithms.- Neuro-Fuzzy Based Nonlinear Models.- Recon.gurable Pattern Generators Using Nonlinear Electronic Circuits.- Condfiguring A Non-Linear Process Control System Using Virtual Instrumentation.

Leseproben

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.