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Tracking Control of Uncertain High-Order Nonlinear Systems with Odd Rational Powers and the Dead-Zone Input:A Direct Fuzzy Adaptive Control Method

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Journal of Systems Science & Complexity 2022年第5期35卷 1685-1699页

作者： LIU Zhenguo SHI Yuyuan WU Yuqiang School of Automation and Software Engineering Shanxi UniversityTaiyuan 030006China Institute of Automation Qufu Normal UniversityQufu 273165China

This work studies the tracking issue of uncertain nonlinear *** existence of odd rational powers,multiple unknown parameters and the dead-zone input add many difficulties for control *** procedures of the control design,by introducing an appropriate Lyapunov function,utilizing recursive control method and the inequality technique,some appropriate intermediate auxiliary control laws are designed under the hypothesis that nonlinear terms in the system are *** those nonlinear terms are unknown,by employing the powerful approximation ability of fuzzy systems,the intermediate auxiliary control laws are approximated recursively and used to construct the virtual ***,a new fuzzy adaptive tracking controller is constructed to ensure a small tracking error and the boundedness of all *** this paper,the overparameterization problem is significantly avoided since only two adaptive laws are *** and practical examples are used to verify the raised theory.

关键词： Dead-zone input direct fuzzy adaptive control uncertain systems

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Graph Neural Networks and Fault Tree Analysis for Runtime Dynamic Risk Assessment of Autonomous Systems 71

Graph Neural Networks and Fault Tree Analysis for Runtime Dy...

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71st Annual Reliability and Maintainability Symposium, RAMS 2025

作者： Grimstad, Joachim Morozov, Andrey Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70550 Germany

ISBN: (纸本)9798350367744

To our knowledge, this paper offers two novel contributions in this field. First, it addresses the need for runtime dynamic risk assessment in autonomous systems by proposing a framework that enables continuous risk evaluation in complex and dynamic environments at runtime. This framework uses dynamic and inductive graph neural network methods that update and predict during runtime to reflect changes in the environment and can process large amounts of data efficiently. Secondly, it integrates traditional quantitative risk models, to introduce some degree of explainability of the graph neural network by converting the graph into traditional models such as fault trees. The second contribution is the partial proof-of-concept of the proposed concept by training a graph attention network to predict top event probabilities of simple fault trees represented as graphs. The implementation tries simple off-the-shelf algorithms but conducts some initial experimentation with loss functions, dropout layers, and distribution shifted datasets to evaluate the generalization capability of the models. Initial results show promise, demonstrating the potential of using GATs to predict top event probabilities in fault tree analysis. Future research should focus on optimizing dropout probabilities, expanding datasets, and exploring hyperparameter optimization. Additionally, investigating the necessary node features and subdividing graphs for better model handling are key areas for further exploration. Additionally, other methods such as GraphSAGE might be more suitable, and a comparison should be made. This work paves the way for integrating advanced machine learning techniques with traditional risk assessment methodologies, contributing to the development of safer, more reliable, and partially explainable autonomous systems. © 2025 IEEE.

关键词： Graph neural networks

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Case Study: ROS-Based Fault Injection for Risk Analysis of Robotic Manipulator 19

Case Study: ROS-Based Fault Injection for Risk Analysis of R...

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19th IEEE International Conference on automation Science and engineering, CASE 2023

作者： Ma, Yuliang Grimmeisen, Philipp Morozov, Andrey The Institute of Industrial Automation and Software Engineering University of Stuttgart Germany

ISBN: (纸本)9798350320695

Industrial cyber-physical systems (ICPS) are becoming more complex due to increasing behavioral and structural complexity. This increases the likelihood of faults, errors, and failures. This can lead to economic losses and even hazardous events. Fault injection is an efficient method to estimate the potential risk of safety-critical ICPS. In this paper, we propose a new fault injection-based risk analysis method for Robot Operating System (ROS) and demonstrate its applicability with a robot manipulator case study. We conducted extensive fault injection experiments using a pick-and-place task. We injected two types of sensor signal faults: bias and noise. First, fault injections were implemented on a ROS/Gazebo model of the manipulator with randomly selected fault parameters such as fault type, location, magnitude, and duration. The experiments helped to identify potential failure scenarios and to find critical fault locations. The most important factor contributing to system failures was the operational phase during which the faults were injected. We then tested our fault injection method on a real Franka Emika Panda collaborative manipulator to validate the effectiveness of the proposed ROS-based fault injection method. We observed that the digital model showed similar behavior to the real manipulator. © 2023 IEEE.

关键词： Cyber Physical System

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Hybrid Approaches and Datasets for Remaining Useful Life Prediction: A Review 57

Hybrid Approaches and Datasets for Remaining Useful Life Pre...

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18th IFAC Workshop on Time Delay Systems, TDS 2024

作者： Artelt, Maurice Weiß, Matthias Dittler, Daniel Goersch, Yannick Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany

The prediction of the remaining service life is a widely researched topic. A precise forecast of the remaining service life enables optimising the total cost of ownership, resulting in better utilization of the resources already used as well as the avoidance of cost-intensive unplanned downtimes. Especially in the increasingly important second life movement for resources that have already been produced, the accurate prediction of the remaining service life is a decisive factor. Data is always required for modelling a prediction. On the one hand, these can be used for the classic determination of tabular book values for physical models. On the other hand, it can also be used for a data science project to create a data-driven modelling approach. This paper presents the results of the literature review on the use of methods for data-driven prediction of remaining service life. It considers which methods were used in the individual papers as well as the statements about the underlying database. Therefore, the scope of this survey is to explore recent data-driven approaches for remaining useful life prediction. As such, "remaining service life" was used as a keyword and the abstracts were screened for the modelling approaches and data used. Subsequently, this key information was categorized and evaluated. The results of the literature review show that the majority of papers use data-driven modelling to predict the remaining service life being dominated by LSTM, CNN and particle filtering, followed by physical modelling. Most papers use a public dataset to showcase their research results like the well-known C-MAPSS, bearing or Lithium-Ion Battery dataset. The minority uses hybrid modelling. The on this field published paper favoured addressing data scarcity, mitigate overfitting and improve prediction accuracy. © 2024 The Authors.

关键词： Prediction models

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Flexible Co-Simulation Approach for Model Adaption in Digital Twins of Power-to-X Platforms 29

Flexible Co-Simulation Approach for Model Adaption in Digita...

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

作者： Dittler, Daniel Stauss, David Rentschler, Philipp Stümpfle, Johannes Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany Institute for Micro Process Engineering Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350361230

A Digital Twin of a production plant comprises a variety of simulation models. When these models are coupled for co-simulation, they can reflect the behavior of the entire production plant and be used in various application scenarios. To leverage the benefits of the Digital Twin and simulation during the operational phase, manual efforts in model adaption must be automated. This contribution presents a flexible co-simulation approach that enables automated selection of simulation tool interfaces, parameterization, and execution of different model configurations. Finally, application scenarios in the context of Power-to-X production are discussed, and the current implementation of the approach is presented. © 2024 IEEE.

关键词： Digital elevation model

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A Novel Architecture for Robust and Adaptive Machine Learning Using Heterogeneous Data in Condition Monitoring of automation Systems 28

A Novel Architecture for Robust and Adaptive Machine Learnin...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Kamm, Simon Suthandhira, Paveen Rajai Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (纸本)9798350339918

Machine learning implementations in an industrial setting poses various challenges due to the heterogeneous nature of the data sources. A classical machine learning algorithm cannot adapt to dynamic changes in the environment, such as the addition, removal, or failure of a data source. However, to handle heterogeneous data and the challenges coming with this, it is a mandatory capability to build robust and adaptive machine learning models for industrial applications. In this work, a novel architecture for robust and adaptive machine learning is proposed to address these challenges. For this, an architecture consisting of different modular layers is developed, where different models can be easily plugged in. The architecture can handle heterogeneous data with different fusion techniques, which are discussed and evaluated in this paper. The proposed architecture is then evaluated on two public datasets for condition monitoring of automation systems to prove its robustness and adaptiveness. The architecture is compared with baseline models and shows more robust performance in case of failing/removed data sources. In addition, new data sources can easily be added without the need to retrain the whole model. Furthermore, the architecture can detect and locate faulty data sources. © 2023 IEEE.

关键词： Data fusion

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Towards autonomous system: Flexible modular production system enhanced with large language model agents 28

Towards autonomous system: Flexible modular production syste...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Xia, Yuchen Shenoy, Manthan Jazdi, Nasser Weyrich, Michael University of Stuttgart Institute of Industrial Automation and Software Engineering Stuttgart Germany

ISBN: (纸本)9798350339918

In this paper, we present a novel framework that combines large language models (LLMs), digital twins and industrial automation system to enable intelligent planning and control of production processes. We retrofit the automation system for a modular production facility and create executable control interfaces of fine-granular functionalities and coarse-granular skills. Low-level functionalities are executed by automation components, and high-level skills are performed by automation modules. Subsequently, a digital twin system is developed, registering these interfaces and containing additional descriptive information about the production system. Based on the retrofitted automation system and the created digital twins, LLM-agents are designed to interpret descriptive information in the digital twins and control the physical system through service interfaces. These LLM-agents serve as intelligent agents on different levels within an automation system, enabling autonomous planning and control of flexible production. Given a task instruction as input, the LLM-agents orchestrate a sequence of atomic functionalities and skills to accomplish the task. We demonstrate how our implemented prototype can handle un-predefined tasks, plan a production process, and execute the operations. This research highlights the potential of integrating LLMs into industrial automation systems in the context of smart factory for more agile, flexible, and adaptive production processes, while it also underscores the critical insights and limitations for future work. Demos at: https://***/YuchenXia/GPT4Industrialautomation © 2023 IEEE.

关键词： Autonomous agents

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Continuous Analysis and Optimization of Vehicle software Updates using the Intelligent Digital Twin 28

Continuous Analysis and Optimization of Vehicle Software Upd...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Weiß, Matthias Müller, Manuel Dettinger, Falk Jazdi, Nasser Weyrich, Michael Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (纸本)9798350339918

An increasing proportion of software characterizes modern automated systems such as vehicles. It can be used to provide new functions to improve the customer experience in terms of automation, personalization, and connectivity. For system development, this means that classic processes must be rethought and supplemented by new strategies. This is mapped via the trend of the software-defined systems. In this context, individual components can be improved over the entire life cycle. In order for these updates to function properly, testing before deployment is commonplace. However, since the software is complex and it is infeasible to test 100% of the possible scenarios, a software update can still lead to unforeseen side effects. At the same time, it must be monitored whether the desired update effects actually apply. In the worst case, the side effects include errors that must be found as quickly as possible in order not to negatively influence product quality and subsequent innovation cycles. The goal of this work is to detect side effects that manifest themselves as anomalies at runtime and to trace them back to the corresponding code change. For this purpose, a new approach is presented that uses the digital twin of the vehicle as an information base, providing operational data and simulations parallel to operation. The simulation generates synthetic data also containing the rare and risky cases that can then be matched against the operational data to identify anomalies. Once an anomaly is detected, a subsequent analysis follows that relates the anomaly to a causal code sequence. The presented approach is realized using the example of a model vehicle with microcontrollers and corresponding peripherals such as sensors and motors. The model vehicle is simulated using the simulation software SimulIDE, which provides the synthetic data. The evaluation shows that the trace back of side effects to the respective code sequences can successfully be achieved. © 2023 IEEE.

关键词： Vehicles

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Synthetic Data Generation for improving Deep Learning-based 5G Indoor Positioning 28

Synthetic Data Generation for improving Deep Learning-based ...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Vietz, Hannes Hirth, Manuel Baum, Sebastian Weyrich, Michael University Stuttgart Institute of Industrial Automation and Software Engineering Stuttgart Germany

ISBN: (纸本)9798350339918

Gathering sufficient labeled training data to effectively train a high-performing deep learning model can be particularly challenging in the realm of industrial automation. Depending on the data type, this may require expensive interruptions to production processes or similar disruptions on the factory floor for data collection. It is often uncertain which data types are crucial for enhancing the performance of the trained model. For vision models, factors such as specific viewing angles or lighting conditions may be important, while for models utilizing radio signals, unique reflections generated by moving metal surfaces could be significant. Moreover, data labeling is expensive as it is primarily conducted manually by human workers. This paper demonstrates how to automatically generate relevant, labeled synthetic training data to boost a neural network's accuracy for deep learning-based 5G indoor positioning tasks. We reveal that employing this generated synthetic data to train a convolutional neural network can improve its median positioning accuracy by a notable 25%. © 2023 IEEE.

关键词： Generative adversarial networks

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Automated and Continuous Risk Assessment for ROS-Based software-Defined Robotic Systems 19

Automated and Continuous Risk Assessment for ROS-Based Softw...

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19th IEEE International Conference on automation Science and engineering, CASE 2023

作者： Grimmeisen, Philipp Golwalkar, Rucha Ma, Yuliang Morozov, Andrey The Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart70550 Germany

ISBN: (纸本)9798350320695

In modern and complex production systems, the focus is shifted toward the software part. software-Defined Manufacturing (SDM) and Cyber-Physical Production Systems (CPPS) characterize this trend. SDM and CPPS enable the concept of adaptive, flexible, and self-configuring production systems. These software-intensive robotic systems are safety-critical because they usually are applied in the same environments as human workers. Therefore they require a continuous risk assessment. The uploading of a new software to the system can change its behavior drastically and therefore, the risk assessment needs to be redone. Key enabling technologies are digital twins, advanced and hybrid risk models, and Model-to-Model (M2M) transformation methods. In this paper, we introduce a new approach to the automated and continuous risk assessment based on Robot Operating System (ROS) code of a software-defined robotic system. The approach pipelines four key elements: (i) a logger that logs the data of the digital twin, (ii) an adder algorithm that creates risk annotated code based on the given ROS code, the output of the logger, and the hardware description including risk data of robot parts, (iii) an M2M transformation algorithm that automatically generates hybrid risk models from risk-annotated code, and (iv) OpenPRA solvers for numerical evaluation of the generated hybrid risk models. © 2023 IEEE.

关键词： Risk assessment

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