Iterative-Based Optimal-Inverse Feedforward for Output-Tracking of Nonminimum-Phase Systems

Precision positioning plays a significant role in nonminimum-phase systems, especially toward biomedical applications. Precision output-tracking for a pre-specified output-trajectory can be obtained by iteratively modifying the control input based on previous cycle data of the output-tracking error. This paper presents the implementation of iterative control together with optimal inversion-based feedforward that further improves the output-tracking performance of nonminimum-phase systems. Experimental results for the piezo-based flexible structure system are provided to illustrate the improvements

Frequency-Domain Data-Driven Adaptive Iterative Learning Control Approach: With Application to Wafer Stage

The feedforward control is becoming increasingly important in ultra-precision stages. However, the conventional model-based methods cannot achieve expected performance in new-generation stages since it is hard to obtain the accurate plant model due to the complicated stage dynamical properties. To tackle this problem, this article develops a model-free data-driven adaptive iterative learning approach that is designed in the frequency-domain. Explicitly, the proposed method utilizes the frequency-response data to learn and update the output of the feedforward controller online, which has benefits that both the structure and parameters of the plant model are not required. An unbiased estimation method for the frequency response of the closed-loop system is proposed and proved through the theoretical analysis. Comparative experiments on a linear motor confirm the effectiveness and superiority of the proposed method, and show that it has the ability to avoid the performance deterioration caused by the model mismatch with the increasing iterative trials

Improvement in the Imaging Performance of Atomic Force Microscopy: A Survey

Nanotechnology is the branch of science which deals with the manipulation of matters at an extremely high resolution down to the atomic level. In recent years, atomic force microscopy (AFM) has proven to be extremely versatile as an investigative tool in this field. The imaging performance of AFMs is hindered by: 1) the complex behavior of piezo materials, such as vibrations due to the lightly damped low-frequency resonant modes, inherent hysteresis, and creep nonlinearities; 2) the cross-coupling effect caused by the piezoelectric tube scanner (PTS); 3) the limited bandwidth of the probe; 4) the limitations of the conventional raster scanning method using a triangular reference signal; 5) the limited bandwidth of the proportional-integral controllers used in AFMs; 6) the offset, noise, and limited sensitivity of position sensors and photodetectors; and 7) the limited sampling rate of the AFM's measurement unit. Due to these limitations, an AFM has a high spatial but low temporal resolution, i.e., its imaging is slow, e.g., an image frame of a living cell takes up to 120 s, which means that rapid biological processes that occur in seconds cannot be studied using commercially available AFMs. There is a need to perform fast scans using an AFM with nanoscale accuracy. This paper presents a survey of the literature, presents an overview of a few emerging innovative solutions in AFM imaging, and proposes future research directions.This work was supported in part by the Australian Research Council (ARC) under Grant FL11010002 and Grant DP160101121 and the UNSW Canberra under a Rector's Visiting Fellowshi

Performance-driven control of nano-motion systems

The performance of high-precision mechatronic systems is subject to ever increasing demands regarding speed and accuracy. To meet these demands, new actuator drivers, sensor signal processing and control algorithms have to be derived. The state-of-the-art scientific developments in these research directions can significantly improve the performance of high-precision systems. However, translation of the scientific developments to usable technology is often non-trivial. To improve the performance of high-precision systems and to bridge the gap between science and technology, a performance-driven control approach has been developed. First, the main performance limiting factor (PLF) is identified. Then, a model-based compensation method is developed for the identified PLF. Experimental validation shows the performance improvement and reveals the next PLF to which the same procedure is applied. The compensation method can relate to the actuator driver, the sensor system or the control algorithm. In this thesis, the focus is on nano-motion systems that are driven by piezo actuators and/or use encoder sensors. Nano-motion systems are defined as the class of systems that require velocities ranging from nanometers per second to millimeters per second with a (sub)nanometer resolution. The main PLFs of such systems are the actuator driver, hysteresis, stick-slip effects, repetitive disturbances, coupling between degrees-of-freedom (DOFs), geometric nonlinearities and quantization errors. The developed approach is applied to three illustrative experimental cases that exhibit the above mentioned PLFs. The cases include a nano-motion stage driven by a walking piezo actuator, a metrological AFM and an encoder system. The contributions of this thesis relate to modeling, actuation driver development, control synthesis and encoder sensor signal processing. In particular, dynamic models are derived of the bimorph piezo legs of the walking piezo actuator and of the nano-motion stage with the walking piezo actuator containing the switching actuation principle, stick-slip effects and contact dynamics. Subsequently, a model-based optimization is performed to obtain optimal drive waveforms for a constant stage velocity. Both the walking piezo actuator and the AFM case exhibit repetitive disturbances with a non-constant period-time, for which dedicated repetitive control methods are developed. Furthermore, control algorithms have been developed to cope with the present coupling between and hysteresis in the different axes of the AFM. Finally, sensor signal processing algorithms have been developed to cope with the quantization effects and encoder imperfections in optical incremental encoders. The application of the performance-driven control approach to the different cases shows that the different identified PLFs can be successfully modeled and compensated for. The experiments show that the performance-driven control approach can largely improve the performance of nano-motion systems with piezo actuators and/or encoder sensors

Model reference control for ultra-high precision positioning systems

Due to the increasing demands of high-density semiconductors, molecular biology, optoelectronics, and MEMS/NEMS in the past decades, control of ultra-high precision positioning using piezoelectricity has become an important area because of its high displacement resolution, wide bandwidth, low power consumption, and potential low cost. However, the relatively small displacement range limits its application. This work proposed a practical ultra-high precision piezoelectric positioning system with a complementary high displacement range actuation technology. Solenoids are low cost, high speed electromagnetic actuators which are commonly used in on-off mode only because of the inherent high nonlinear force-stroke characteristics and unipolar forces (push/pull) generated by the magnetic fields. In this work, an integrated positioning system based on a monolithic piezoelectric positioner and a set of push-pull dual solenoid actuators is designed for high speed and high precision positioning applications. The overall resolution can be sub-nanometer while the moving range is in millimeters, a three order of magnitude increase from using piezoelectric positioner alone. The dynamic models of the dual solenoid actuator and piezoelectric nanopositioner are derived. The main challenge of designing such positioning systems is to maintain the accuracy and stability in the presence of un-modeled dynamics, plant variations, and parasitic nonlinearities, specifically in this work, the friction and forcestroke nonlinearities of the dual solenoid actuator, and the friction, hysteresis and coupling effects of piezoelectric actuator, which are impossible to be modeled accurately and even time-varying. A model reference design approach is presented to attenuate linear as well as nonlinear uncertainties, with a fixed order controller augmenting a reference model that embeds the nominal dynamics of the plant. To improve transient characteristics, a Variable Model Reference Zero Vibration (VMRZV) control is also proposed to stabilize the system and attenuate the adverse effect of parasitic nonlinearities of micro-/nano- positioning actuators and command-induced vibrations. The speed of the ultra-high precision system with VMRZV control can also be quantitatively adjusted by systematically varying the reference model. This novel control method improves the robustness and performance significantly. Preliminary experimental data on dual solenoid system confirm the feasibility of the proposed method

Scanning Probe Microscopies for the Study at Nanoscale of Nanomaterials and Nanosystems: Magnetic Properties for Bio-applications

Magnetic nanomaterials due to their various features different from the ordinary bulk matter in their mechanical, thermal, magnetic, optical properties, are attracting more and more attention in both theoretical research and practical applications in various fields. Magnetic nanoparticles (MNPs) are a very important branch of magnetic nanomaterials due to their nanoscale sizes, being relatively long in vivo half-life and limited agglomeration. These make them ideal for biomedical applications such as magnetic labeling, hyperthermia cancer treatment, targeted drug delivery, and contrast enhancement agents in magnetic resonance imaging (MRI). In drug delivery applications, MNPs can be determined with high accuracy [1]. It would be of interest to localize and characterize MNPs at the nanoscale for biological applications. However, very limited studies exist on detecting and characterizing the magnetic signals of nanoparticles in biological science. Many methods in surface structure analysis are used as nano-characterization techniques, such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), field electron microscopy (FEM), field ion microscope (FIM), low energy electron diffraction (LEED), Auger electron spectroscopy (AES), photoelectron spectroscopy (ESCA) and electron probe. These techniques detect the surface or interface to show the physical and chemical properties at the nanoscale. But any kind of these techniques has the limitations of one kind or another. For example, LEED and X-ray diffraction method require that the sample has a periodic structure; the resolution of optical microscopy and SEM are insufficient to distinguish surface atoms; high-resolution TEM is mainly used for thin bulk samples and interfacial studies to detect the magnetic properties, but the sample preparation process to get cell sections for TEM analysis is time consuming, and only a small part of cell section can be analyzed; FEM and FIM can only detect the tip radius of less than 100 nm of the atomic structure in two-dimensional geometry. Most commonly, studies which analyze the magnetic nature of MNPs use a superconducting quantum interference device (SQUID) and vibrating sample magnetometer (VSM). But due to low sensitivity and ultimately poor accuracy neither is an appropriate technique to measure the magnetic moment of individual MNPs, whatever in air or in liquid environment. Proper characterization and monitoring the properties of MNPs system are important for their potential applications. Currently, one of the most common methods for intracellular imaging of magnetic nanoparticles is fluorescence microscopy [2]. A disadvantage of this technique is that nanoparticles must first be labeled with fluorescent probes in order to be visualized. Due to the inherent limitations, the resolution of optical instruments is restricted by the wavelength of the light [3]. In 2010, Sun et al. conjugated fluorescent probes to the surface of magnetic nanoparticles to map cellular uptake pathways [4]. Relative to fluorescence microscopy, two-photon microscopy (TPM) offers improved resolution to study cellular interactions with magnetic nanoparticles, requiring the particles to be labeled with a two-photon fluorescent dye [5]. However it has been known that the imaging depth in TPM cannot be increased indefinitely, meanwhile optimization of the two-photon excitation efficiency is limited by the degree of damage the specimen can tolerate [6]. Due to the relatively poor resolution and reliability of these techniques, scanning probe microscopes (SPM) emerged out. SPM is a generation of scanning tunneling microscope based on a variety of new probe microscopes, such as atomic force microscopy (AFM), lateral force microscopy (LFM) and electrostatic force microscope (EFM). Among these techniques magnetic force microscope (MFM), a label-free in vitro detection method for magnetic materials, has the capability to detect nanoscale magnetic domains and simultaneously obtain atomic force microscopy topography images. Due to its ability to localize, characterize and distinguish magnetic materials from other materials at the nanoscale, as well as the advantage of three-dimensional information, MFM offers the great potential for the in vivo research. The scope for MFM lies in detecting the presence of magnetic nanomaterials and spatially localizing magnetic domains. It is likely that magnetic nanomaterials (occur in clusters or aggregates) are embedded in a biological matrix to different depth, and surrounded by bio-molecules. The development and application of MFM for detecting MNPs hold great promise in biology. Spatially localizing magnetic plaques, at nanometer resolution in ambient atmospheric environment, will provide a better understanding of the deposition mechanism of magnetic material derivatives in the biological tissues. The background on magnetic materials and nanoparticles is presented in chapter 1 and AFM/MFM experimental apparatus and technique is illustrated in chapter 2. In the last three chapters of the thesis the results of three different typologies of experiments are reported. The studies I have conducted are developed in the framework of the research activities of the laboratory of Scanning Probe Microscopy of EMiNaLab (coordinator prof. Marco Rossi), at the Department of Basic and Applied Sciences for Engineering of Sapienza University of Rome. In particular, in Chapter 3, we investigate bacterial biofilms at the first time, which are colonies of microbes embedded in a self-produced exopolysaccharides extracellular matrix presenting a major concern in health care. We will demonstrate an approach based on magnetic force microscopy to perform accurate measurement of the thickness of soft thin films - although it may easily extended even to stiff films - deposited on periodically patterned magnetic substrates. By detecting the biofilm thickness MFM will provide a novel method to study the thin film. In the second part of the thesis, MFM is applied to visualize and quantitatively measure magnetically labeled vesicular system. Vesicles containing magnetic nanoparticles as magnetic target carrier can be used for a wide range of biological application. The encapsulation of drugs in vesicles can minimize drug degradation and inactivation by increasing drug bioavailability and targeting to the pathological area. Many different non-contact techniques have been proposed. Nevertheless, MFM has never been used to study vesicular systems embedding MNPs, either qualitatively or quantitatively. MFM will be illustrated to evaluate the amount of MNPs incorporated in single vesicle, together with discussion on its merits and possible sources of uncertainty. In the last part of the thesis, we developed the capability of AFM/MFM to detect magnetically labeled materials of biological interest, which are magnetoferritin, APTES functionalized Fe3O4 nanoparticles and cells labeled Fe@Au nanoparticle. AFM/MFM will allow us to detect magnetic nanoparticles within submembranes and without severe deformation of samples. In our study, We expect to demonstrate the potential of MFM for the study of magnetic properties of different nano-biosystems, illustrating our approaches which aim at deducing quantitative information from MFM characterizations. Such a research is useful for future applications of MFM, indicating the potential to image magnetic nanoparticles unlabelled and unmodified in living cellular systems. The overall target of the thesis is to develop and standardize reliable innovative protocols, using scanning probe microscopy-based techniques that could be implemented in rapid and early theranostic methods

Discrete Modeling and Sliding Mode Control of Piezoelectric Actuators

With the ability to generate fine displacements with a resolution down to sub-nanometers, piezoelectric actuators (PEAs) have found wide applications in various nano-positioning systems. However, existence of various effects in PEAs, such as hysteresis and creep, as well as dynamics can seriously degrade the PEA performance or even lead to instability. This raises a great need to model and control PEAs for improved performance, which have drawn remarkable attention in the literature. Sliding mode control (SMC) shows its potential to the control of PEA, by which the hysteresis and other nonlinear effects can be regard as disturbance to the dynamic model and thus rejected or compensated by its switching control. To implement SMC in digital computers, this research is aimed at developing novel discrete models and discrete SMC (DSMC)-based control schemes for PEAs, along with their experimental validation. The first part of this thesis concerns with the modeling and control of one-degree of freedom (DOF) PEA, which can be treated as a single-input-single-output (SISO) system. Specifically, a novel discrete model based on the concept of auto-regressive moving average (ARMA) was developed for the PEA hysteresis; and to compensate for the PEA hysteresis and improve its dynamics, an output tracking integrated discrete proportional-integral-derivative-based SMC (PID-SMC) was developed. On this basis, by making use of the availability of PEA hysteresis models, two control schemes, named “the discrete inversion feedforward based PID-SMC” and “the discrete disturbance observer (DOB)-based PID-SMC”, were further developed. To illustrate the effectiveness of the developed models and control schemes, experiments were designed and conducted on a commercially available one-DOF PEA, as compared with the existing ones. The second part of the thesis presents the extension of the developed modeling and control methods to multi-DOF PEAs. Given the fact that details with regard to the PEA internal configurations is not typically provided by the manufacturer, a state space model based on the black box system identification was developed for the three-DOF PEA. The developed model was then integrated in the output tracking based discrete PID-SMC, with its effectiveness verified through the experiments on a commercially available three-DOF PEA. The superiority of the proposed control method over the conventional PID controller was also experimentally investigated and demonstrated. Finally, by integrating with a DOB in the discrete PID-based SMC, a novel control scheme is resulted to compensate for the nonlinearities of the three-DOF PEA. To verify its effectiveness, the discrete DOB based PID-SMC was applied in the control experiments and compared with the existing SMC. The significance of this research lies in the development of the discrete models and PID-based SMC for PEAs, which is of great help to improve their performance. The successful application of the proposed method in the control of multi-DOF PEA allows the application of SMC to the control of complicated multi-inputs-multi-outputs (MIMO) systems without details regarding the internal configuration. Also, integration of the inversion based feedforward control and the DOB in the SMC design has been proven effective for the tracking control of PEAs