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European control Conference (ECC)

作者： Taouba Jouini Astrid Bensmann Torsten Lilge Richard Hanke-Rauschenbach Matthias Müller Department of Automatic Control Hanover Germany Institute of Electric Power Systems Hanover Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We study the dispatching of multi-modal energy systems (MMES) from a sequential market perspective based on hierarchical Model Predictive control (MPC). In a sequential setting, an upper level determines the purchased electrical power from the day-ahead market, followed by a lower-level MPC responsible for the dispatching of the multi-energy system according to the continuous trading. Our case study consists in an MMES in Hanover, Germany, with electrical and heat demands as well as photovoltaic and wind energy generation, storage and coupling elements. We show that the hierarchical MPC solution can be embedded within the European market and German market area to provide a judicious dispatching of the MMES, also under imperfect uncertainty forecast. In particular, we discuss a reasonable choice for the prediction horizons and the effect of the forecast on the total incurring cost.

关键词： Wind energy generation Photovoltaic systems Uncertainty Costs Europe Dispatching Real-time systems

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SYSDYNET - A MATLAB App and Toolbox for Dynamic Network Identification ⁎ , ⁎⁎

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IFAC-PapersOnLine 2024年第15期58卷 574-579页

作者： Paul M.J. Van den Hof Shengling Shi Harm H.M. Weerts Xiaodong Cheng Karthik R. Ramaswamy Arne G. Dankers H.J. (Mannes) Dreef Stefanie J.M. Fonken Tom R.V. Steentjes Job B.T. Meijer Control Systems Group Department of Electrical Engineering Eindhoven University of Technology The Netherlands

Identification in interconnected systems requires the handling of phenomena that go beyond the classical open-loop and closed-loop type of identification problems. Over the last decade a comprehensive theory has been developed for addressing identification problems in linear dynamic networks, formulated in a module framework, where the network structure is characterized by a directed graph in which nodes are signals and links are transfer functions. The resulting methods and approaches have been collected in a MATLAB App and Toolbox, supported by an attractive graphical user interface that provides an interactive workflow for manipulating the structural properties of dynamic networks, applying basic network operations like immersion and module invariance testing, and for investigating network/module generic identifiability and selecting appropriate predictor model inputs and outputs. The workflow supports the allocation of external excitation signals (actuation) and measured node signals (sensing) so as to achieve generic identifiability and provide consistent estimation of target modules. The Toolbox includes algorithms for actual network simulation and identification.

关键词： System identification identifiability dynamic networks interconnected systems

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Risk-Aware MPC for Stochastic systems with Runtime Temporal Logics

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IFAC-PapersOnLine 2024年第11期58卷 93-98页

作者： M.H.W. Engelaar Z. Zhang M. Lazar S. Haesaert Department of Electrical Engineering (Control Systems Group) Eindhoven University of Technology The Netherlands

This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime. Conventional risk-aware control typically assumes that all specifications are predefined and remain unchanged during runtime. In this paper, we propose a novel, provably correct model predictive control scheme for linear systems with additive unbounded stochastic disturbances that dynamically evaluates the feasibility of runtime signal temporal logic specifications and automatically reschedules the control inputs accordingly. The control method guarantees the probabilistic satisfaction of newly accepted specifications without sacrificing the satisfaction of the previously accepted ones. The proposed control method is validated by a robotic motion planning case study.

关键词： Linear stochastic systems Probabilistic constraints Real-time control Stochastic model predictive control Temporal logic

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Predicting AGV Battery Cell Voltage Using a Neural Network Approach with Preliminary Data Analysis and Processing

Predicting AGV Battery Cell Voltage Using a Neural Network A...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Pavliuk, Olena Medykovskyy, Mykola Steclik, Tomasz Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Lviv Polytechnic National University Automated Control Systems Department Lviv Ukraine

ISBN: (纸本)9798350324457

Modern methods for solving the AGV battery cell voltage prediction problem include a symbiosis of probabilistic and machine learning methods. In this study, we propose to use two RNN-based approaches with preliminary data analysis and processing. Five filters were selected for smoothing: Moving average;Weighted moving average;Exponential smoothing;Smoothing with local regression;Lowess smoothing. RNN forecasting was performed on the basis of a centered stationary signal obtained by subtracting the value obtained with the five filters from the original *** forecast accuracy was evaluated using the following errors: MAPE, RMSE, MSPE, RMSPE, MFE, MBE, SEBIAS, ME. Adequacy of the proposed neural network model based on the determination coefficient R2. The coefficient of determination for all the centered signals obtained using the above five methods yielded adequate predictive models. Exponential smoothing (MAPE=0.002;RMSE=141.481;R2=0.94) and LS (MAPE=0.002;RMSE=124.239;R2:0.96) proved to be the most effective methods for short-term forecasts. Moving average (MAPE=0.00207;RMSE=146.589;R2=0.94) is recommended for long-term forecasts with additional smoothing of the forecasted values because of the noise in them. © 2023 IEEE.

关键词： AGV battery cell voltage data pre-processing forecast Machine Learning

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EMOTION RECOGNITION USING BIOMEDICAL SIGNALS IN A MULTIMODAL EMOTION ANALYSIS SYSTEM FOR SOCIAL ROBOTS 38

EMOTION RECOGNITION USING BIOMEDICAL SIGNALS IN A MULTIMODAL...

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38th Annual European Simulation and Modelling Conference, ESM 2024

作者： Skowroński, Kamil Galuszka, Adam Probierz, Eryka Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Silesian University of Technology Gliwice Poland Lukasiewicz Research Network Institute of Innovative Technologies EMAG Katowice Poland

ISBN: (纸本)9789492859334

The main purpose of this paper is to review available datasets and artificial intelligence algorithms for the problem of emotion recognition using biomedical signals and to implement the best-adapted solution to a multimodal emotion analysis system. The review takes into account the practical requirements for using solutions in a multimodal emotion analysis system for social robots - the ease of use of devices for collecting biomedical data and the method of categorizing data according to Paul Ekman’s theory. The latest 6 datasets from recent years were reviewed for the given problem in terms of physiological signals, emotion categorization, number of people and stimuli. To better illustrate the work in the given field, a comparison was made of 5 different solutions containing classic artificial intelligence algorithms and popular neural networks. Ultimately, one dataset was selected on which several of the compared solutions were tested and implemented into a multimodal emotion analysis system. The obtained comparison results indicate that despite a large number of diverse datasets from recent years, there are still few publicly available solutions with very high accuracy, especially in the case of neural networks. The proposed issues and solutions may draw more attention to the improvement of social robot systems responsible for communication and companionship. © 2024 Modelling and Simulation 2024 - 38th Annual European Simulation and Modelling Conference 2024, ESM 2024. All rights reserved.

关键词： Social robots

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Predefined-Time Distributed Fault-Tolerant control for Nonlinear Multiagent systems Suffering From Nonaffine Faults

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3300-3311页

作者： Cheng, Shuai Xin, Bin Chen, Jie Deng, Fang Qingdao University School of Automation Qingdao266071 China Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing100081 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Department of Control Science and Engineering Shanghai200092 China

This article investigates the distributed predefined-time (PT) fault-tolerant control for nonlinear multiagent systems (NNMSs) with nonaffine faults. A novel distributed PT control scheme is proposed based on a new PT theorem. The switching functions are introduced into distributed control signals to avoid the singularity problem. The second-order filter technology is designed to solve "explosion of complexity" issues. The newly designed PT compensation signals are utilized to eliminate filter errors. The proposed distributed PT controller based on local information can guarantee that all signals of the closed-loop system are PT bounded, and consensus errors of NNMSs can be enforced to a small region around the origin within a predefined time. Compared with the finite/fixed time, the predefined time is an exact value given in advance, which remains constant regardless of initial states and control parameters. Finally, two comparative simulations are provided to demonstrate the presented strategy. © 2013 IEEE.

关键词： Adaptive control systems

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Fail-Safe Decision Architecture for Positioning Failures on Automated Vehicles 9

Fail-Safe Decision Architecture for Positioning Failures on ...

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9th IEEE Smart World Congress, SWC 2023

作者： Rodriguez-Arozamena, Mario Aranguren-Mendieta, Inigo Perez, Joshue Zubizarreta, Asier Tecnalia Basque Research and Technology Alliance Bilbao Derio Spain Basque Research and Technology Alliance Industry and Mobility Unit Tecnalia Derio Spain Dept. of Automatic Control and Systems Engineering Bilbao Spain

ISBN: (纸本)9798350319804

Precise localization is essential for the operation of Connected and Automated Vehicles (CAVs) in urban scenarios. Camera and LiDAR-based solutions are currently used in some of the CAVs around the world, but they entail an expensive performance in terms of computational time and economic cost. Due to this, Global Navigation Satellite System (GNSS) based solutions remains the preferred solution for positioning Automated Vehicles in different Operational Design Domains (ODDs). Although several improvements in system reliability have been made recently, in case of positioning failures most of the architectures rely on system redundancy or automation disengagements to achieve minimal risk conditions. This paper presents a fail-safe decision architecture for mitigating positioning failures of automated vehicles in urban scenarios. The proposed architecture utilizes a combination of sensor fusion localization and decision-making modules to ensure the safe and efficient operation of the vehicle in the event of a positioning failure. The proposed approach is evaluated through simulation in a representative urban scenario and is shown to effectively handle positioning failures, improving the localization accuracy provided by each information source. © 2023 IEEE.

关键词： automated driving fail-safe operation fallback strategy positioning failure safe state

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Reinforcement learning-based unknown reference tracking control of HMASs with nonidentical communication delays

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Science China(Information Sciences) 2023年第7期66卷 46-57页

作者： Yong XU Zheng-Guang WU Wei-Wei CHE Deyuan MENG School of Automation Beijing Institute of Technology Institute of Cyber-Systems and Control Zhejiang University College of Mathematics and Computer Science Zhejiang Normal University Department of Automation Qingdao University School of Automation Science and Electrical Engineering Beihang University(BUAA)

This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems(HMASs) subject to nonidentical communication delays by a reinforcement learning *** with existing studies assuming that the precise model of the leader is globally or distributively accessible to all or some of the followers, the leader's precise dynamical model is entirely inaccessible to all the followers in this paper. A data-based learning algorithm is first proposed to reconstruct the leader's unknown system matrix online. A distributed predictor subject to communication delays is further devised to estimate the leader's state, where interaction delays are allowed to be nonidentical. Then, a learning-based local controller, together with a discounted performance function, is projected to reach the optimal output synchronization. Bellman equations and game algebraic Riccati equations are constructed to learn the optimal solution by developing a model-based reinforcement learning(RL) algorithm online without solving regulator equations, which is followed by a model-free off-policy RL algorithm to relax the requirement of all agents' dynamics faced by the model-based RL algorithm. The optimal tracking control of HMASs subject to unknown leader dynamics and communication delays is shown to be solvable under the proposed RL algorithms. Finally, the effectiveness of theoretical analysis is verified by numerical simulations.

关键词： heterogeneous multiagent systems HMAS reinforcement learning RL optimal output synchronization communication delays

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Robust Data-Driven Tube-Based Zonotopic Predictive control with Closed-Loop Guarantees 63

Robust Data-Driven Tube-Based Zonotopic Predictive Control w...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Farjadnia, Mahsa Fontan, Angela Alanwar, Amr Molinari, Marco Johansson, Karl Henrik Kth Royal Institute of Technology Department of Energy Technology Sweden Kth Royal Institute of Technology Division of Decision and Control Systems Sweden Technical University of Munich School of Computation Information and Technology Germany

ISBN: (纸本)9798350316339

This work proposes a robust data-driven tube-based zonotopic predictive control (TZPC) approach for discrete-time linear systems, designed to ensure stability and recursive feasibility in the presence of bounded noise. The proposed approach consists of two phases. In an initial learning phase, we provide an over-approximation of all models consistent with past input and noisy state data using zonotope properties. Subsequently, in a control phase, we formulate an optimization problem, which by integrating terminal ingredients is proven to be recursively feasible. Moreover, we prove that implementing this data-driven predictive control approach guarantees robust exponential stability of the closed-loop system. The effectiveness and competitive performance of the proposed control strategy, compared to recent data-driven predictive control methods, are illustrated through numerical simulations. © 2024 IEEE.

关键词： Predictive control systems

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Holistic Self-Distillation with the Squeeze and Excitation Network for Fine-grained Plant Pathology Classification

Holistic Self-Distillation with the Squeeze and Excitation N...

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International Conference on Information Fusion

作者： Jingxuan Su Sean Anderson Lyudmila Mihaylova Department of Automatic Control & Systems Engineering University of Sheffield UK

Fine-grained plant pathology classification is an important task for precision agriculture, but at the same time, it is challenging due to the subtle difference in plant categories. Variances in the lighting conditions, position, and stages of disease symptoms usually lead to degradation of classification accuracy. Knowledge distillation is a popular method to improve the model performance to deal with the indistinguishable image classification problem. It aims to have a well-optimised small student network guided by a large teacher network. Existing knowledge distillation methods mainly consider training a teacher network that needs a high storage space and considerable computing resources. Self-knowledge distillation methods have been proposed to distil knowledge from the same network. Although self-knowledge distillation saves time and space compared with knowledge distillation, it only learns label knowledge. In this paper, we propose a novel self-distillation method to recognize the fine-grained plant category, which considers holistic knowledge based on the Squeeze and Excitation Network. We label this new method as holistic self-distillation because it captures knowledge through spatial features and labels. The performance validation of the proposed approach is performed on two public fine-grained plant datasets: Plant Pathology 2021 and Plant Pathology 2020 with the accuracy of 98.22% and 90.72% respectively. We also present experiments on the state-of-the-art algorithm (ResNet-50). The classification results demonstrate the effectiveness of the proposed approach with respect to accuracy.

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