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IFAC-PapersOnLine 2022年第7期55卷 673-678页

作者： Erika Oliveira-Silva Cesar de Prada Daniel Navia Department of Systems Engineering and Automatic Control School of Industrial Engineering University of Valladolid Dr. Mergelina s/n 47011 Valladolid Spain Institute of Sustainable Processes Dr. Mergelina s/n 47011 Valladolid Spain Dpto. Ingeniería Química y Ambiental Universidad Técnica Federico Santa María Avd. Vicuña Mackenna Campus San Joaquín Santiago Chile

The present paper shows the development of a dynamic rigorous model of a propylene-propane splitter connected to an industrial MPC controller via OPC-UA. The model includes the material and energy balances, thermodynamic equilibrium, and constitutive equations. Some of the PI controllers presented in the real plant have also been modeled. A dynamic model requires further information as the sizing of the equipments, heat transfer coefficients, thermodynamics data and a good initial value for the state and algebraic variables. The validation of the model was performed in open loop and in closed loop simulations. The simulation results were compared to the historical data of the process. As future work, the simulation platform created will be used to study new algorithms of RTO with Modifier Adaptation methodology.

关键词： Real-time Optimization superfractionator Aspen DMC MPC

CrowdSim2: an Open Synthetic Benchmark for Object Detectors

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arXiv 2023年

作者： Foszner, Pawel Szczęsna, Agnieszka Ciampi, Luca Messina, Nicola Cygan, Adam Bizon, Bartosz Cogiel, Michal Golba, Dominik Macioszek, Elżbieta Staniszewski, Michal Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Institute of Information Science and Technologies National Research Council Pisa Italy QSystems.pro sp. z o.o. Mochnackiego 34 Bytom41-907 Poland Blees sp. z o.o. Zygmunta Starego 24a/10 Gliwice44-100 Poland Department of Transport Systems Traffic Engineering and Logistics Faculty of Transport and Aviation Engineering Silesian University of Technology Katowice Poland

Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in Computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new scenarios never seen during training and may not be adequately tested in non-ordinary yet crucial real-world situations. This paper presents and publicly releases CrowdSim2, a new synthetic collection of images suitable for people and vehicle detection gathered from a simulator based on the Unity graphical engine. It consists of thousands of images gathered from various synthetic scenarios resembling the real world, where we varied some factors of interest, such as the weather conditions and the number of objects in the scenes. The labels are automatically collected and consist of bounding boxes that precisely localize objects belonging to the two object classes, leaving out humans from the annotation pipeline. We exploited this new benchmark as a testing ground for some state-of-the-art detectors, showing that our simulated scenarios can be a valuable tool for measuring their performances in a controlled environment. © 2023, CC BY.

关键词： Object detection

Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods

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arXiv 2023年

Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA. © 2023, CC BY.

关键词： Deep learning

controllability Analysis of Multi-Modal Acoustic Particle Manipulation in One-Dimensional Standing Waves

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arXiv 2025年

作者： Wu, Dongjun Perticarari, Guilherme Baasch, Thierry Department of Automatic Control Lund University Box 118 LundSE-221 00 Sweden Master’s in Machine Learning Systems and Control programme Lund University Sweden Department of Biomedical Engineering Lund University Box 118 LundSE-221 00 Sweden

Acoustic manipulation in microfluidic devices enables contactless handling of biological cells for Lab-on-Chip applications. This paper analyzes the controllability of multi-particle systems in a one-dimensional acoustic standing wave system using multi-modal actuation. By modeling the system as a nonlinear control system, we analyze its global and local controllability, quantifying these properties in terms of mode numbers. Our results show that sufficient modes enable dense reachability sets, while mode mixing with 10 modes grants a strict notion of controllability to 80% of the state space in a two-particle system. These findings offer theoretical insights for designing acoustic manipulation algorithms, supporting efficient control in biomedical applications. © 2025, CC BY.

关键词： controllability

Convergence analysis of stochastic higher-order majorization-minimization algorithms

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arXiv 2021年

作者： Lupu, Daniela Necoara, Ion Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics Applied Mathematics of the Romanian Academy Bucharest050711 Romania

Majorization-minimization schemes are a broad class of iterative methods targeting general optimization problems, including nonconvex, nonsmooth and stochastic. These algorithms minimize successively a sequence of upper bounds of the objective function so that along the iterations the objective value decreases. We present a stochastic higher-order algorithmic framework for minimizing the average of a very large number of sufficiently smooth functions. Our stochastic framework is based on the notion of stochastic higher-order upper bound approximations of the finite-sum objective function and minibatching. We derive convergence results for nonconvex and convex optimization problems when the higher-order approximation of the objective function yields an error that is p times differentiable and has Lipschitz continuous p derivative. More precisely, for general nonconvex problems we present asymptotic stationary point guarantees and under Kurdyka-Lojasiewicz property we derive local convergence rates ranging from sublinear to linear. For convex problems with uniformly convex objective function we derive local (super)linear convergence results for our algorithm. Compared to existing stochastic (first-order) methods, our algorithm adapts to the problem's curvature and allows using any batch size. Preliminary numerical tests support the effectiveness of our algorithmic framework. © 2021, CC BY.

关键词： Iterative methods

Efficiency of stochastic coordinate proximal gradient methods on nonseparable composite optimization

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arXiv 2021年

作者： Necoara, Ion Chorobura, Flavia Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania

This paper deals with composite optimization problems having the objective function formed as the sum of two terms, one has Lipschitz continuous gradient along random subspaces and may be nonconvex and the second term is simple and differentiable, but possibly nonconvex and nonseparable. Under these settings we design a stochastic coordinate proximal gradient method which takes into account the nonseparable composite form of the objective function. This algorithm achieves scalability by constructing at each iteration a local approximation model of the whole nonseparable objective function along a random subspace with user-determined dimension. We outline efficient techniques for selecting the random subspace, yielding an implementation that has low cost per-iteration while also achieving fast convergence rates. We present a probabilistic worst-case complexity analysis for our stochastic coordinate proximal gradient method in convex and nonconvex settings, in particular we prove high-probability bounds on the number of iterations before a given optimality is achieved. Extensive numerical results also confirm the efficiency of our algorithm. © 2021, CC BY.

关键词： Gradient methods

Technical and Economic Assessment of Use Local Autonomous Sources of Energy Supply 25

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Technical and Economic Assessment of Use Local Autonomous So...

25th IEEE International Conference on Problems of Automated Electric Drive. Theory and Practice, PAEP 2020

作者： Zachepa, Iurii Chornyi, Oleksii Perekrest, Andrii Zachepa, Nataliia Zbyrannyk, Oksana Mykhalchenko, Ganna Kremenchuk Mykhailo Ostrohradskyi National University Systems of Automatic Control and Electric Drive Department Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Economics and Management Kremenchuk Ukraine Ukrainian Engineering Pedagogics Academy Department of Economy of Enterprises and Management Kharkov Ukraine

ISBN: (纸本)9781728199351

The paper proves the economic feasibility of using a local autonomous source of energy supply, which is formed from electrical equipment of other functional purposes based on an asynchronous machine with capacitive self-excitation. To assess the technical and economic indicators of the effectiveness of introducing a local autonomous source of energy supply, the discounted method is adopted, where an indicator of savings from possible losses due to the exclusion of labor resources from production is used as an additional influence factor. © 2020 IEEE.

关键词： Energy resources

Bounding the difference between model predictive control and neural networks

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arXiv 2022年

作者： Drummond, Ross Duncan, Stephen R. Turner, Matthew C. Pauli, Patricia Allgöwer, Frank Department of Engineering Science University of Oxford 17 Parks Road OxfordOX1 3PJ United Kingdom Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70569 Germany

There is a growing debate on whether the future of feedback control systems will be dominated by data-driven or model-driven approaches. Each of these two approaches has their own complimentary set of advantages and disadvantages, however, only limited attempts have, so far, been developed to bridge the gap between them. To address this issue, this paper introduces a method to bound the worst-case error between feedback control policies based upon model predictive control (MPC) and neural networks (NNs). This result is leveraged into an approach to automatically synthesize MPC policies minimising the worst-case error with respect to a NN. Numerical examples highlight the application of the bounds, with the goal of the paper being to encourage a more quantitative understanding of the relationship between data-driven and model-driven control. © 2022, CC BY.

关键词： Model predictive control

A Nonparametric Bayesian Compressive Sensing Classification

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Journal of Advances in Information Fusion 2020年第1期15卷 57-70页

作者： Chen, Ruilong Hawes, Matthew Mihaylova, Lyudmila Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom

This paper presents a novel nonparametric backpropagation Bayesian compressive sensing (BBCS) classification approach. While the state-of-the-art parametric classifiers such as logistic regression require model training and can result in inadequate models, the developed approach does not require model training. It is combined with a column-based subspace sampling process and can deal efficiently with uncertainties and highly computational tasks. Validation on a publicly available vehicle logo dataset shows that the proposed classifier can achieve up to 98% recognition accuracy as compared with the state-ofthe- art nonparametric *** with the generic Bayesian compressive sensing classification, the proposed approach decreases the mean number of misclassifications by 87% along with 68% reduction of the computational time. The robustness of the BBCS approach is demonstrated over scene recognition tasks, and its outperformance over the AlexNet convolutional neural network algorithm is demonstrated in noisy conditions. The proposed BBCS approach is generic and can be used in different areas;for example, it has shown robustness over the CIFAR-10 dataset. © 2020 JAIF.

关键词： Compressed sensing

Adaptive stabilization of minimum-phase systems under quantized measurements

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IFAC-PapersOnLine 2020年第2期53卷 3761-3766页

作者： Anton Selivanov Alexander Fradkov Department of Automatic Control and Systems Engineering The University of Sheffield UK Saint Petersburg University Russia Institute for Problems of Mechanical Engineering Russian Academy of Sciences Russia

We construct an adaptive controller for a linear minimum-phase system of an arbitrary relative degree with an unknown bounded disturbance and dynamically quantized measurements. The key novelty is the extension of the shunting method (parallel feedforward compensator) to plants with bounded disturbances. This method leads to an augmented system of relative degree one that is stabilized by a passification-based adaptive controller. Moreover, we design a switching procedure for the controller parameters and the quantizer’s zoom that ensures the state convergence from an arbitrary set to an ellipsoid whose size depends on the disturbance bound. The results are demonstrated by an example of an aircraft flight control.

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