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第43届中国控制会议

作者： Alexander Domyshev Elena Chistyakova Aliona Dreglea Denis Sidorov Fang Liu Energy Systems Institute Siberian Branch Russian Academy Sciences Irkutsk National Research Technical University Institute for System Dynamics and Control Theory Siberian Branch Russian Academy Sciences

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The method for controlling a DC-DC converter is proposed to ensures the high quality control at large fluctuations in load currents by using differential gain control coefficients and second derivative control. Various implementations of balancing the currents of a multiphase DC-DC converter are discussed, with a focus on achieving accurate current regulation without introducing additional delay in the control system. Stochastic particle swarm optimization method is used to find optimal values of the PID controller parameters. An automatic constraint-handling in optimization are also discussed as relevant techniques in the field.

关键词： DCDC Optimization Load Large Fluctuations Stabilization Rauss-Hurwitz criterion

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Simulation of Nonlinear Dynamic Processes with Volterra Polynomials and Continued Fractions

Simulation of Nonlinear Dynamic Processes with Volterra Poly...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Svetlana Solodusha Yuliya Kokonova Matrosov Institute for System Dynamics and Control Theory Melentiev Energy Systems Institute of SB RAS Irkutsk Russia Irkutsk National Research Technical University Irkutsk Russia

In this paper, we propose a technique for increasing the accuracy of modeling nonlinear dynamics of input-output systems using finite segments (polynomials) of Volterra integral power series. We highlight a case when the Volterra kernel identification problem is solved. The linear part of the Volterra polynomials of power N is formed as an equivalent continued fraction. Assuming that the corresponding Volterra kernel is known with some error, such an approach allows us to obtain a model with a new linear part that depends on the error. At the same time, prognostic properties of the integral model can be improved by the using the connection coefficient. The problem of indentifying such a parameter was solved using a special extremal problem. We analyzed the performance of the approach for some dynamic systems. Tests examples were used to compare our approach with the known modeling method that employs a series of Volterra polynomials whose transient responses are tuned to different test signal heights.

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Efficient structure-preserving support tensor train machine

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 98-119页

作者： Kirandeep Kour Peter Benner Sergey Dolgov Martin Stoll Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Mathematical Sciences University of Bath Bath UK Faculty of Mathematics Technische Universität Chemnitz Chemnitz Germany

An increasing amount of the collected data are high-dimensional multi-way arrays (tensors), and it is crucial for efficient learning algorithms to exploit this tensorial structure as much as possible. The ever present curse of dimensionality for high dimensional data and the loss of structure when vectorizing the data motivates the use of tailored low-rank tensor classification methods. In the presence of small amounts of training data, kernel methods offer an attractive choice as they provide the possibility for a nonlinear decision boundary. We develop the Tensor Train Multi-way Multi-level Kernel (TT-MMK), which combines the simplicity of the Canonical Polyadic decomposition, the classification power of the Dual Structure-preserving Support Vector Machine, and the reliability of the Tensor Train (TT) approximation. We show by experiments that the TT-MMK method is usually more reliable computationally, less sensitive to tuning parameters, and gives higher prediction accuracy in the SVM classification when benchmarked against other state-of-the-art techniques.

关键词： tensor decomposition support vector machine kernel approximation high-dimensional data classification

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Data-Driven Predictive Disturbance Observer for Quasi Continuum Manipulators

Data-Driven Predictive Disturbance Observer for Quasi Contin...

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IEEE Conference on Decision and control

作者： Daniel Müller Justinus Feilhauer Jennifer Wickert Julian Berberich Frank Allgöwer Oliver Sawodny Institute for System Dynamics University of Stuttgart Stuttgart Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Deriving accurate and time efficient models for soft robots has proven to be a difficult task. This is mainly due to the behaviour of the soft materials and the interaction with other parts of the robot, resulting in complicated models with many states that require complex sensor *** robots such as the Bionic Soft Arm (BSA) are subject to uncertainties. The resulting mismatch between plant and model can be viewed as a disturbance. Disturbance observers significantly improve the control performance of the model-based controllers. However, they assume an underlying model of the disturbance that is usually unknown. To address this problem, we propose a novel Data-Driven Predictive Disturbance Observer (DPDO), which predicts the future disturbance based on past measurements that are updated online and, thereby, enables the model-based controller to achieve zero control error. The approach is first tested in simulation, examining the influence of noise, time delays and typical model uncertainties for the BSA. Afterwards, the algorithm is applied to the real system, demonstrating its practicability.

关键词： Uncertainty Trajectory tracking Biological system modeling Soft robotics Predictive models Prediction algorithms Manipulators

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A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids

arXiv

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arXiv 2024年

作者： Khorrami, Mohammad S. Goyal, Pawan Mianroodi, Jaber R. Svendsen, Bob Benner, Peter Raabe, Dierk Microstructure Physics and Alloy Design Max-Planck Institute for Sustainable Materials Düsseldorf Germany Computational Methods in Systems and Control Theory Max-Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Material Mechanics RWTH Aachen University Aachen Germany

The purpose of the current work is the development of a so-called physics-encoded Fourier neural operator (PeFNO) for surrogate modeling of the quasi-static equilibrium stress field in solids. Rather than accounting for constraints from physics in the loss function as done in the (now standard) physics-informed approach, the physics-encoded approach incorporates or"encodes" such constraints directly into the network or operator architecture. As a result, in contrast to the physics-informed approach in which only training is physically constrained, both training and output are physically constrained in the physics-encoded approach. For the current constraint of divergence-free stress, a novel encoding approach based on a stress potential is proposed. As a"proof-of-concept" example application of the proposed PeFNO, a heterogeneous polycrystalline material consisting of isotropic elastic grains subject to uniaxial extension is considered. Stress field data for training are obtained from the numerical solution of a corresponding boundary-value problem for quasi-static mechanical equilibrium. This data is also employed to train an analogous physics-guided FNO (PgFNO) and physics-informed FNO (PiFNO) for comparison. As confirmed by this comparison and as expected on the basis of their differences, the output of the trained PeFNO is significantly more accurate in satisfying mechanical equilibrium than the output of either the trained PgFNO or the trained PiFNO. Copyright © 2024, The Authors. All rights reserved.

关键词： Polycrystalline materials

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Soft sensor for monitoring dynamic changes in cell composition

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IFAC-PapersOnLine 2022年第23期55卷 98-103页

作者： Sebastián Espinel-Ríos Bruno Morabito Katja Bettenbrock Steffen Klamt Rolf Findeisen Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Bioprocess Engineering Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

Precise and accurate online monitoring methods are needed to enable smart biomanufacturing and automation. Most of the sensors available focus on process parameters such as metabolite and dissolved gas concentrations, cell density or viability, among other variables like pH, temperature, etc. In this work, we develop a soft sensor algorithm to estimate the cell composition online, a very important aspect often overlooked in the bioprocess monitoring literature. Our strategy is based on full information estimation, an optimization-based estimator that takes into account the dynamics of the cell metabolism and considers all the available measurements from the beginning of the process, thus it has a memory effect. Being able to track dynamic changes in cell composition can open the door to promising applications, e.g., predictive control and automation of biosystems. As a case study, we consider the Escherichia co li’ s metabolism growing on glycerol under different levels of oxygen supply. We compare the performance of our soft sensor method against resource balance analysis, a previously proposed estimator based on steady-state assumptions. Overall, the presented full information estimator was able to track the dynamic changes in cell composition significantly more accurately. We also discuss how our estimation strategy can be transformed into a moving horizon estimation, where only the available measurements in a fixed and moving window are considered, thereby reducing possible computational burdens.

关键词： Cell composition soft sensor full information estimation resource balance analysis dynamic enzyme-cost flux balance analysis

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Cell-free biosynthesis meets dynamic optimization and control: a fed-batch framework

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IFAC-PapersOnLine 2022年第23期55卷 92-97页

作者： Sebastián Espinel-Ríos Nicolas Huber Edgar Alberto Alcalá-Orozco Bruno Morabito Thomas F.T. Rexer Udo Reichl Steffen Klamt Rolf Findeisen Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Bioprocess Engineering Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

Cell-free biosynthesis uses the machinery of cells, such as the metabolic reactions, to carry out conversion processes in vitro. This can be more beneficial than in vivo approaches like fermentations. Some advantages of these synthetic biology processes include higher product yields, rates and titers, and more flexibility in pathway design. Cell-free biosynthesis is still in early stages and, unlike in vivo production, there are very few examples of model-based optimization. Moreover, we encounter static optimizations in most cases, neglecting the dynamic nature of the processes. We present an optimal control framework to maximize the efficiency of cell-free biosynthesis. We focus on fed-batch setups as they allow enhancing the reaction rates via the feeding, extending production processes for longer times, and minimizing the potential negative effects of enzyme kinetics with substrate inhibition. Our framework can in principle handle several cost functions and exploit both static and dynamic degrees of freedom. An aspect that can hinder model-based optimization is model uncertainty, which can arise due to uncertain parameters, oversimplified model assumptions or unknown reaction mechanisms. To counteract this, we propose the use of model predictive control during the process operation. In addition, we outline the use of moving horizon estimation as an observer in the case of unmeasured states. We consider the de novo cell-free synthesis of uridine diphosphate-N-acetylglucosamine as a biomedical relevant case study, where we were able to maximize the volumetric productivity in simulations, and indirectly also the titer and enzyme efficiency use.

关键词： Cell-free fed-batch optimization control state estimation UDP-GlcNAc

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Adaptively restarted block Krylov subspace methods with low-synchronization skeletons

arXiv

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arXiv 2022年

作者： Lund, Kathryn Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstr. 1 Magdeburg39106 Germany

With the recent realization of exascale performace by Oak Ridge National Laboratory’s Frontier supercomputer, reducing communication in kernels like QR factorization has become even more imperative. Low-synchronization Gram-Schmidt methods, first introduced in [K. Swirydowicz, ´ J. Langou, S. Ananthan, U. Yang, and S. Thomas, Low Synchronization Gram-Schmidt and Generalized Minimum Residual Algorithms, Numer. Lin. Alg. Appl., Vol. 28(2), e2343, 2020)], have been shown to improve the scalability of the Arnoldi method in high-performance distributed computing. Block versions of low-synchronization Gram-Schmidt show further potential for speeding up algorithms, as column-batching allows for maximizing cache usage with matrix-matrix operations. In this work, low-synchronization block Gram-Schmidt variants from [E. Carson, K. Lund, M. Rozložník, and S. Thomas, Block Gram-Schmidt algorithms and their stability properties, Lin. Alg. Appl., 638, pp. 150–195, 2022] are transformed into block Arnoldi variants for use in block full orthogonalization methods (BFOM) and block generalized minimal residual methods (BGMRES). An adaptive restarting heuristic is developed to handle instabilities that arise with the increasing condition number of the Krylov basis. The performance, accuracy, and stability of these methods are assessed via a flexible benchmarking tool written in MATLAB. The modularity of the tool additionally permits generalized block inner products, like the global inner *** Codes 65F10, 65F25, 65F50, 15-04 Copyright © 2022, The Authors. All rights reserved.

关键词： Stability

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Parametric S-Parameter Prediction Using Deep Learning

Parametric S-Parameter Prediction Using Deep Learning

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IEEE Topical Meeting on Electrical Performance of Electronic Packaging (EPEPS)

作者： Vinayak Bansal Lihong Feng Valentin de la Rubia Peter Benner Department of Chemical Engineering Indian Institute of Technology Delhi India Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Departamento de Mateádtica Aplicada a las TIC Universidad Politécnica de Madrid Madrid Spain

We construct a neural network model of S-parameters, from which the S-parameters can be quickly pre-dicted. Numerical tests on a filter model show that the proposed method accurately predicts the S-parameters with mul... 详细信息

ISBN: (数字)9798350351231

ISBN: (纸本)9798350351248

关键词： Deep learning Accuracy Transfer functions Predictive models Stability analysis Scattering parameters Numerical models Feedforward neural networks Numerical stability Electronics packaging

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Qualitative properties and stability analysis of the mathematical model for a DC-DC electric circuit

arXiv

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arXiv 2024年

作者： Chistyakova, E.V. Sidorov, D.N. Domyshev, A.V. Chistyakov, V.F. Institute for System Dynamics and Control Theory Siberian Branch Russian Academy Sciences Irkutsk Russia Irkutsk National Research Technical University Russia Energy Systems Institute Siberian Branch Russian Academy Sciences Irkutsk Russia Harbin Institute of Technology Harbin China

This paper describes a simplified model of an electric circuit with a DC-DC converter and a PID-regulator as a system of integral differential equations with an identically singular matrix multiplying the higher derivative of the desired vector-function. We use theoretical results on integral and differential equations and their systems to prove solvability of such a model and analyze its *** Codes 34K35 © 2024, CC BY.

关键词： Choquet integral

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