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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Parbat, Shreyas Iqbal, Imam Viedt, Isabell Urbas, Leon Technische Universität Dresden Process Control Systems & Process Systems Engineering Group Dresden Germany

ISBN: (纸本)9798350361230

Waste and recycling material sorting is crucial for reducing environmental impact and promoting resource recovery. However, its complexity poses significant challenges, necessitating the development of effective sorting processes. Manual operation of these plants can be less efficient than automated systems. The first step toward automation is utilizing a Digital Twin, which combines fundamental principles and data-driven insights into the waste sorting process. To achieve this, the equipment involved in waste sorting plants can be analyzed in detail, focusing on operational settings and their impact on overall efficiency. Initially, a steady-state model of the plant is developed, followed by the implementation of advanced strategies like model predictive control. The model can be rigorously tested and refined using a case study on German post-consumer waste. The architecture of the Digital Twin, comprising various building blocks such as the modeling and simulation block, is being developed to transition away from manual operations. This Digital Twin aims to enhance sorting efficiency through offline and online optimization of operational set points, leading to a more sustainable and resource-efficient future. Through simulations and real-time data integration, a Digital Twin for the waste and recycling material process can aid with process design, fine-tuning, and plant automation. © 2024 IEEE.

关键词： Circular economy

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Reachability analysis for piecewise affine systems with neural network-based controllers 63

Reachability analysis for piecewise affine systems with neur...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Teichrib, Dieter Darup, Moritz Schulze Tu Dortmund University Control and Cyberphysical Systems Group Department of Mechanical Engineering Germany

ISBN: (纸本)9798350316339

Neural networks (NN) have been successfully applied to approximate various types of complex control laws, resulting in low-complexity NN -based controllers that are fast to evaluate. However, when approximating control laws using NN, performance and stability guarantees of the original controller may not be preserved. Recently, it has been shown that it is possible to provide such guarantees for linear systems with NNbased controllers by analyzing the approximation error with respect to a stabilizing base-line controller or by computing reachable sets of the closed-loop system. The latter has the advantage of not requiring a base-line controller. In this paper, we show that similar ideas can be used to analyze the closed-loop behavior of piecewise affine (PWA) systems with an NNbased controller. Our approach builds on computing overapproximations of reachable sets using mixed-integer linear programming, which allows to certify that the closed-loop system converges to a small set containing the origin while satisfying input and state constraints. We also show how to modify a given NN-based controller to ensure asymptotic stability for the controlled PWA system. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Predictable Interval MDPs through Entropy Regularization 63

Predictable Interval MDPs through Entropy Regularization

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Van Zutphen, M.J.T.C. Delimpaltadakis, G. Heemels, W.P.M.H. Antunes, D.J. Eindhoven University of Technology Control Systems Technology Group Department of Mechanical Engineering Netherlands

ISBN: (纸本)9798350316339

Regularization of control policies using entropy can be instrumental in adjusting predictability levels of real-world systems. Applications benefiting from such approaches range from cybersecurity, which aims at maximal unpredictability, to human-robot interaction, where predictable behavior is highly desirable. In this paper, we consider entropy regularization for interval Markov decision processes (IMDPs), which are uncertain MDPs where transition probabilities are only known to belong to specified intervals. Lately, IMDPs have gained significant popularity in the context of abstracting stochastic systems for control design. In this work, we address robust minimization of the linear combination of entropy and a standard cumulative cost in IMDPs, thereby establishing a trade-off between optimality and predictability. We show that optimal deterministic policies exist, and devise a value-iteration algorithm to compute them. The algorithm solves a number of convex programs at each step. Finally, through an illustrative example we show the benefits of penalizing entropy in IMDPs. © 2024 IEEE.

关键词： Stochastic systems

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Detection of Oscillations in Process control Loops From Visual Image Space Using Deep Convolutional Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 982-995页

作者： Tao Wang Qiming Chen Xun Lang Lei Xie Peng Li Hongye Su the Department of Electronic Engineering the School of InformationYunnan UniversityKunming 650091China DAMO Academy Alibaba GroupHangzhou 311100China the State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou 310027China

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant *** numerous automatic detection techniques have been proposed,most of them can only address part of the practical *** oscillation is heuristically defined as a visually apparent periodic ***,manual visual inspection is labor-intensive and prone to missed *** neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction *** this work,an exploration of the typical CNN models for visual oscillation detection is ***,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation *** feasibility and validity of this framework are verified utilizing extensive numerical and industrial *** with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and *** addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

关键词： Convolutional neural networks(CNNs) deep learning image processing oscillation detection process industries

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Encrypted system identification as-a-service via reliable encrypted matrix inversion 63

Encrypted system identification as-a-service via reliable en...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Adamek, Janis Binfet, Philipp Schluter, Nils Darup, Moritz Schulze Tu Dortmund University Control and Cyber-physical Systems Group Department of Mechanical Engineering Germany

ISBN: (纸本)9798350316339

Encrypted computation opens up promising avenues across a plethora of application domains, including machine learning, health-care, finance, and control. Arithmetic homomorphic encryption, in particular, is a natural fit for cloud-based computational services. However, computations are essentially limited to polynomial circuits, while comparisons, transcendental functions, and iterative algorithms are notoriously hard to realize. Against this background, the paper presents an encrypted system identification service enabled by a reliable encrypted solution to least squares problems. More precisely, we devise an iterative algorithm for matrix inversion and present reliable initializations as well as certificates for the achieved accuracy without compromising the privacy of provided I/O-data. The effectiveness of the approach is illustrated with three popular identification tasks. © 2024 IEEE.

关键词： Encryption algorithms

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Deadtime Compensation-Based Approximate Decoupling control Approach for Multivariable Processes

Deadtime Compensation-Based Approximate Decoupling Control A...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Aldhandi, Suresh Detroja, Ketan P. Indian Institute of Technology The Systems and Control Group Department of Electrical Engineering Telangana Hyderabad502285 India

ISBN: (纸本)9798350350821

The interaction effects among individual loops and multiple time delays are the prime reasons for degrading the closed-loop performance of multivariable systems. For highly interacting systems, decouplers are used to counteract interaction effects. However, decouplers may not be realizable and even if they are realizable, for high dimensional systems, decoupler elements may be complex. To improve the decoupler realizability, a dead-time compensation (DTC) approach is proposed in this work. The DTC framework can minimise the impact of dead-time on system dynamics. The proposed decoupler is designed based on the dynamic relative gain array (DRGA) to compensate for interaction effects. The DRGA reveals more insights about the coupling effect between a pair of input-output loops. The DRGA elements are used as decoupler elements for the multivariable process. The minimization of both interaction effects and dead-time dynamics can be achieved with the proposed DTC-based decoupling control approach. The simple IM C PI tuning rules are utilized to implement controllers for each loop. The simulation study affirming the proposed method provides better desired closed-loop performance, error indices for nominal and plant model mismatches, and robust stability against plant uncertainty over the popular existing decoupler based approaches. © 2024 IEEE.

关键词： Multivariable control systems

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Reference Model based Data-driven control of Power Amplifiers for High-precision Motion systems

Reference Model based Data-driven Control of Power Amplifier...

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Xu, Duo Popescu, Mihai-Serban Lazar, Mircea Eindhoven University of Technology Control Systems Group Department of Electrical Engineering Netherlands

ISBN: (纸本)9798350335446

Motivated by the increasing requirements in positioning precision for lithography applications, this paper analyzes how the position error in a high-precision motion system is affected by the response of the controlled power amplifier that drives the motor. Based on the analysis, guidelines for designing a reference model in continuous or discrete time that satisfies certain requirements that would decrease the overall position error are presented. Then, a data-driven control method, namely Virtual Reference Feedback Tuning (VRFT), is employed to directly synthesize both feedback and feedforward controllers based on the designed reference model. The position error of the motion system with the data-driven controlled power amplifier is compared to the case when classical industrial controllers are employed in the control of the amplifier. If adequate reference models are designed, the VRFT-controlled power amplifier can significantly decrease the position error such that it is close to the ideal position error. All of the presented results are based on simulations. © 2023 IEEE.

关键词： Power amplifiers

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Consensus-based distributed model predictive control applied to modular multilevel converters

Consensus-based distributed model predictive control applied...

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Dreke, Victor Daniel Reyes Duan, Shiqi Lazar, Mircea Eindhoven University of Technology Control Systems Group Department of Electrical Engineering Netherlands

ISBN: (纸本)9798350335446

Modular multilevel converters (MMCs) have the potential to improve the performance of high- and medium-power applications, such as renewable energy generation and fast charging stations. The functioning of MMCs relies on their ability to balance capacitor voltages across all modules. Motivated by the capacitor voltage balancing (CVB) problem, this paper studies consensus-based distributed model predictive control (DMPC) schemes. As a result, we propose an alternative cost function that enables output synchronization of linear systems and, consequently, solves the CVB problem. The advantages of the proposed method are demonstrated by proving its stability properties and comparing its computational time and performance with existing DMPCs. Simulation results for a benchmark multi-agent system example from the literature and an MMC AC/DC topology demonstrate the effectiveness of the developed consensus DMPC. © 2023 IEEE.

关键词： Multi agent systems

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Operation Strategies for modular large-scale alkaline electrolysis systems 22

Operation Strategies for modular large-scale alkaline electr...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Lange, Hannes Schirmer, Christioph Mock, Michael Viedt, Isabell Klose, Anselm Urbas, Leon Process Systems Engineering Group TUD Dresden University of Technology Dresden Germany Process Control Systems TUD Dresden University of Technology Dresden Germany

ISBN: (纸本)9798331527471

In this paper we compare different operation strategies for alkaline electrolyzis (AEL) systems using numeric simulation. We derived general models for mass and energy balances from literature and composed them into a model for an AEL system. Here we considered a numbered-up plant topology, containing multiple AEL stacks with single electrolyte distribution and gas separation systems. The gas impurities and achievable minimal load distribution were compared for the different operating strategies. The results were discussed and further conclusions for the operation of a homogeneous numbered-up AEL system were derived. © 2024 IEEE.

关键词： Electrolysis

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A Machine-Learning Approach for Prognosis of Oscillating Water Column Wave Generators 27

A Machine-Learning Approach for Prognosis of Oscillating Wat...

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27th International Conference on Circuits, systems, Communications and Computers, CSCC 2023

作者： Garrido, Izaskun Lecube, Jon Mzoughi, Fares Aboutalebi, Pavam Ahmad, Irfan Cayuela, Salvador Garrido, Ailtor University of the Basque Country-UPV/EHU Automatic Control Group Acg Department of Automatic Control and Systems Engineering Bilbao Spain

ISBN: (纸本)9798350337594

Wave excitations cause structural vibrations on the Oscillating Water Columns (OWC) lowering the generated power and reducing the life expectancy. The problem of generator deterioration has been considered for the Mutriku MOWC plant and a machine learning-based approach for prognosis and fault characterization has been proposed. In particular, the use of k-Nearest Neighbors (kNN) models for predicting the time to failure of OWC generators has been proposed. The analysis is based on data collected from sensors that measure various operational parameters of the turbines. The results demonstrate that the proposed kNN model is an excellent choice for reducing maintenance costs by enabling scheduling months in advance. The model's high accuracy in predicting generator failures allows for timely and cost-effective maintenance, preventing costly breakdowns and improving turbine efficiency. These results highlight the potential of machine learning-based approaches for addressing maintenance challenges in the energy sector and underscore the importance of proactive strategies in reducing operational costs and maximizing energy production. © 2023 IEEE.

关键词： Cost effectiveness

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