Integrate-and-Differentiate Approach to Nonlinear System Identification

In this paper, we consider a problem of parametric identification of a piece-wise linear mechanical system described by ordinary differential equations. We reconstruct the phase space of the investigated system from accelerometer data and perform parameter identification using iteratively reweighted least squares. Two key features of our study are as follows. First, we use a differentiated governing equation containing acceleration and velocity as the main independent variables instead of the conventional governing equation in velocity and position. Second, we modify the iteratively reweighted least squares method by including an auxiliary reclassification step into it. The application of this method allows us to improve the identification accuracy through the elimination of classification errors needed for parameter estimation of piece-wise linear differential equations. Simulation of the Duffing-like chaotic mechanical system and experimental study of an aluminum beam with asymmetric joint show that the proposed approach is more accurate than state-of-the-art solutions

A NEURAL-BASED MODEL PREDICTIVE CONTROL TO TACKLE STEERING DELAY OF THE IARA AUTONOMOUS CAR

Neste trabalho, propomos uma abordagem de Controle Preditivo Baseado em Modelo Neural (Neural Based Model Predictive Control - N-MPC) para lidar com atrasos na planta de direção de carros autônomos. Examinamos a abordagem N-MPC como uma alternativa para a implementação do subsistema de controle de direção da Intelligent and Autonomous Robotic Automobile (IARA). Para isso, comparamos a solução padrão, baseada na abordagem de controle Proporcional Integral Derivativo (PID), com a abordagem N-MPC. O subsistema de controle de direção PID funciona bem na IARA para velocidades de até 25 km/h. No entanto, acima desta velocidade, atrasos na Planta de Direção da IARA são muito elevados para permitir uma operação adequada usando uma abordagem PID. Modelamos a Planta de Direção da IARA usando uma rede neural e empregamos esse modelo neural na abordagem N-MPC. A abordagem N-MPC superou a abordagem PID reduzindo o impacto de atrasos na Planta de Direção de IARA e permitindo a operação autônoma da IARA em velocidades de até 37 km/h um aumento de 48% na velocidade máxima estáve

Fluid dynamics of bubbly flows

Bubbly flows can be found in many applications in chemical, biological and power engineering. Reliable simulation tools of such flows that allow the design of new processes and optimization of existing one are therefore highly desirable. CFD-simulations applying the multi-fluid approach are very promising to provide such a design tool for complete facilities. In the multi-fluid approach, however, closure models have to be formulated to model the interaction between the continuous and dispersed phase. Due to the complex nature of bubbly flows, different phenomena have to be taken into account and for every phenomenon different closure models exist. Therefore, reliable predictions of unknown bubbly flows are not yet possible with the multi-fluid approach. A strategy to overcome this problem is to define a baseline model in which the closure models including the model constants are fixed so that the limitations of the modeling can be evaluated by validating it on different experiments. Afterwards, the shortcomings are identified so that the baseline model can be stepwise improved without losing the validity for the already validated cases. This development of a baseline model is done in the present work by validating the baseline model developed at the Helmholtz-Zentrum Dresden-Rossendorf mainly basing on experimental data for bubbly pipe flows to bubble columns, bubble plumes and airlift reactors that are relevant in chemical and biological engineering applications. In the present work, a large variety of such setups is used for validation. The buoyancy driven bubbly flows showed thereby a transient behavior on the scale of the facility. Since such large scales are characterized by the geometry of the facility, turbulence models cannot describe them. Therefore, the transient simulation of bubbly flows with two equation models based on the unsteady Reynolds-averaged Navier–Stokes equations is investigated. In combination with the before mentioned baseline model these transient simulations can reproduce many experimental setups without fitting any model. Nevertheless, shortcomings are identified that need to be further investigated to improve the baseline model. For a validation of models, experiments that describe as far as possible all relevant phenomena of bubbly flows are needed. Since such data are rare in the literature, CFD-grade experiments in an airlift reactor were conducted in the present work. Concepts to measure the bubble size distribution and liquid velocities are developed for this purpose. In particular, the liquid velocity measurements are difficult; a sampling bias that was not yet described in the literature is identified. To overcome this error, a hold processor is developed. The closure models are usually formulated based on single bubble experiments in simplified conditions. In particular, the lift force was not yet measured in low Morton number systems under turbulent conditions. A new experimental method is developed in the present work to determine the lift force coefficient in such flow conditions without the aid of moving parts so that the lift force can be measured in any chemical system easily

ESTIMATION AND PREDICTION OF THE HUMAN GAIT DYNAMICS FOR THE CONTROL OF AN ANKLE-FOOT PROSTHESIS

With the growing population of amputees, powered prostheses can be a solution to improve the quality of life for many people. Powered ankle-foot prostheses can be made to behave similar to the lost limb via controllers that emulate the mechanical impedance of the human ankle. Therefore, the understanding of human ankle dynamics is of major significance. First, this work reports the modulation of the mechanical impedance via two mechanisms: the co-contraction of the calf muscles and a change of mean ankle torque and angle. Then, the mechanical impedance of the ankle was determined, for the first time, as a multivariable and time-varying system. These findings reveal the importance of recognizing the state of the user during the gait when the user interacts with the environment. In addition to studying the ankle impedance, a wearable device was designed and evaluated to further the studies on robotic perception for ankle-foot prostheses. This device is capable of characterizing the ground environment and estimating the gait state using visual-inertial sensors. Finally, this study contributes to the field of ankle-foot prostheses by identifying the mechanical behavior of the human ankle and developing a platform to test perception algorithms for the control of robotic prostheses

Ultrafast Ultrasound Imaging

Among medical imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), ultrasound imaging stands out due to its temporal resolution. Owing to the nature of medical ultrasound imaging, it has been used for not only observation of the morphology of living organs but also functional imaging, such as blood flow imaging and evaluation of the cardiac function. Ultrafast ultrasound imaging, which has recently become widely available, significantly increases the opportunities for medical functional imaging. Ultrafast ultrasound imaging typically enables imaging frame-rates of up to ten thousand frames per second (fps). Due to the extremely high temporal resolution, this enables visualization of rapid dynamic responses of biological tissues, which cannot be observed and analyzed by conventional ultrasound imaging. This Special Issue includes various studies of improvements to the performance of ultrafast ultrasoun

Multivariate Statistical Machine Learning Methods for Genomic Prediction

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool

Vibration, Control and Stability of Dynamical Systems

From Preface: This is the fourteenth time when the conference “Dynamical Systems: Theory and Applications” gathers a numerous group of outstanding scientists and engineers, who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without a great effort of the staff of the Department of Automation, Biomechanics and Mechatronics. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and Ministry of Science and Higher Education of Poland. It is a great pleasure that our invitation has been accepted by recording in the history of our conference number of people, including good colleagues and friends as well as a large group of researchers and scientists, who decided to participate in the conference for the first time. With proud and satisfaction we welcomed over 180 persons from 31 countries all over the world. They decided to share the results of their research and many years experiences in a discipline of dynamical systems by submitting many very interesting papers. This year, the DSTA Conference Proceedings were split into three volumes entitled “Dynamical Systems” with respective subtitles: Vibration, Control and Stability of Dynamical Systems; Mathematical and Numerical Aspects of Dynamical System Analysis and Engineering Dynamics and Life Sciences. Additionally, there will be also published two volumes of Springer Proceedings in Mathematics and Statistics entitled “Dynamical Systems in Theoretical Perspective” and “Dynamical Systems in Applications”

Multivariate Statistical Machine Learning Methods for Genomic Prediction

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension. The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool