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Average Communication Rate for Networked Event-Triggered Stochastic control Systems

IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Zengjie Liu, Qingchen Mamduhi, Mohammad H. Hirche, Sandra Eindhoven University of Technology Department of Electrical Engineering Netherlands University of Science and Technology of China Department of Automation Hefei China University of Birmingham School of Computer Science United Kingdom Technical University of Munich Chair of Information-Oriented Control Munich Germany

Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes. For a NET-SCS, the propagation of nonlinear statistics of the network communication status brought up by deterministic thresholds makes the precise computation of ACR difficult. Previous work used to over-simplify the computation using a Gaussian distribution without incorporating this nonlinearity, leading to sacrificed precision. This paper proposes a novel approach to calculate the exact ACR for a NET-SCS using a recursive model. We use theoretical analysis and a numerical study to qualitatively evaluate the deviation gap of the conventional approach that ignores the side information. The accuracy of our proposed method, alongside its comparison with the simplified results of the conventional approach, is validated by experimental studies. Our work is promising to benefit the efficient resource planning of networked control systems with limited communication resources by providing accurate ACR computation. © 2025 IEEE.

关键词： Networked control systems

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State Derivative Normalization for Continuous-Time Deep Neural Networks

arXiv

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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

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Toward Dexterous Hand Functional Movement: Wearable Hybrid Soft Exoglove-FES Study

Toward Dexterous Hand Functional Movement: Wearable Hybrid S...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Hossein Kavianirad Francesco Missiroli Satoshi Endo Lorenzo Masia Sandra Hirche Chair of Information-oriented Control School of Computation Information and Technology Technical University of Munich Munich Germany Assistive Robotics and Interactive ExoSuits (ARIES) Lab Institute for Computer Engineering (ZITI) Heidelberg University Heidelberg Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Functional electrical stimulation (FES), a lightweight wearable technology leveraging active muscle recruitment, can assist functional movement for rehabilitation and various activities of daily living. However, the intricate and user-specific nature of the neuromuscular response to FES, such as the induction of muscular fatigue and discomfort, can lead to incomplete or imprecise functional movements. On the other hand, soft exoskeleton gloves, another form of lightweight wearable technology for hand movement assistance, guide movement externally with higher precision. In this research, therefore, we present a novel hybrid system that combines a soft exoskeleton glove and FES technology. Such an augmented hybrid system could reinforce the advantages of active muscle recruitment as a primary source of actuation with external actuation of the exoskeleton compensating for the limitations of FES in torque generation. Our objective is to investigate the performance of this hybrid system compared to the standalone FES system through experimental evaluation.

关键词： Torque Neuromuscular Exoskeletons Biomechatronics Fatigue Iron Hybrid power systems Neuromuscular stimulation Robots Recruitment

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Time-Robust Path Planning with Piece-Wise Linear Trajectory for Signal Temporal Logic Specifications

Time-Robust Path Planning with Piece-Wise Linear Trajectory ...

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American control Conference (ACC)

作者： Nhan-Khanh Le Erfaun Noorani Sandra Hirche John S. Baras Chair of Information-oriented Control (ITR) School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical and Computer Engineering Institute for Systems Research (ISR) at the University of Maryland College Park MD USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Real-world scenarios are characterized by timing uncertainties, e.g., delays, and disturbances. Algorithms with temporal robustness are crucial in guaranteeing the successful execution of tasks and missions in such scenarios. We study time-robust path planning for synthesizing robots' trajectories that adhere to spatial-temporal specifications expressed in Sig-nal Temporal Logic (STL). In contrast to prior approaches that rely on discretized trajectories with fixed time-steps, we leverage Piece-Wise Linear (PWL) signals for the synthesis. PWL signals represent a trajectory through a sequence of time-stamped waypoints. This allows us to encode the STL formula into a Mixed-Integer Linear Program (MILP) with fewer variables. This reduction is more pronounced for specifications with a long planning horizon. To that end, we define time-robustness for PWL signals. Subsequently, we propose quantitative semantics for PWL signals according to the recursive syntax of STL and prove their soundness. We then propose an encoding strategy to transform our semantics into a MILP. Our simulations showcase the soundness and the performance of our algorithm.

关键词： Uncertainty Semantics Transforms Syntactics Encoding Robustness Trajectory

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Safe Reinforcement Learning via Confidence-Based Filters

Safe Reinforcement Learning via Confidence-Based Filters

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IEEE Conference on Decision and control

作者： Sebastian Curi Armin Lederer Sandra Hirche Andreas Krause Department of Computer Science LAS Group ETH Zurich Switzerland Department of Electrical and Computer Engineering Chair of Information-Oriented Control Technical University of Munich Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Ensuring safety is a crucial challenge when deploying reinforcement learning (RL) to real-world systems. We develop confidence-based safety filters, a control-theoretic approach for certifying state safety constraints for nominal policies learnt via standard RL techniques, based on probabilistic dynamics models. Our approach is based on a reformulation of state constraints in terms of cost functions, reducing safety verification to a standard RL task. By exploiting the concept of hallucinating inputs, we extend this formulation to determine a "backup" policy which is safe for the unknown system with high probability. The nominal policy is minimally adjusted at every time step during a roll-out towards the backup policy, such that safe recovery can be guaranteed afterwards. We provide formal safety guarantees, and empirically demonstrate the effectiveness of our approach.

关键词： Costs Scalability Reinforcement learning Probabilistic logic Cost function Safety control theory

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A Model Predictive Current control for a Three-Phase Modular Matrix Converter

A Model Predictive Current Control for a Three-Phase Modular...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Maryam Sarebanzadeh Mohammad Ali Hosseinzadeh Ralph Kennel Ebrahim Babaei Marco Rivera Patrick Wheeler Chair of High-Power Converters System Technical University of Munich Munich Germany Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Electronic Engineering Faculty of Engineering Chair of Power Electronics Machines and Control (PEMC) Research Group University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Academica Universidad de Talca Talca Chile

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The modular matrix converter (MMC) integrates a multi-winding transformer with a three-phase-to-single-phase matrix converter for each phase, making it suitable for high-power, high-voltage applications. Predictive control is an advanced control technique widely applied to power converters due to its numerous advantages, including fast dynamic response, the elimination of the need for proportional gain, and straightforward implementation. This study proposes a model predictive control (MPC) strategy to regulate load currents, reactive power, and common-mode voltage in a three-phase modular matrix converter. The proposed MPC algorithm selects the optimal switching states from all valid options for the modular matrix converter to apply in the next control step. A cost function is then used to minimize the error between the predicted system variables and their reference values, ensuring that the best states are applied to the modular matrix converter. The proposed MPC technique effectively controls the load currents to follow the desired reference signals, maintains sinusoidal source current waveforms by regulating reactive power, and reduces the magnitude of the common-mode voltage. The performance of the proposed approach is demonstrated through simulation results obtained in the MATLAB/Simulink environment.

关键词： Current control Reactive power Total harmonic distortion Simulation Switches Cost function Transformers Matrix converters Voltage control Predictive control

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Multimodal graph representation learning for robust surgical workflow recognition with adversarial feature disentanglement

引用

Information Fusion 2025年 123卷

作者： Bai, Long Ma, Boyi Wang, Ruohan Wang, Guankun Cui, Beilei Jiang, Zhongliang Islam, Mobarakol Min, Zhe Lai, Jiewen Navab, Nassir Ren, Hongliang Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Chair for Computer Aided Medical Procedures Technical University of Munich Munich Germany Department of Biomedical Engineering University of Toronto Toronto Canada Center for Computational and Molecular Biology Brown University Providence United States UCL Hawkes Institute University College London London United Kingdom School of Control Science and Engineering Shandong University Jinan China

Surgical workflow recognition is vital for automating tasks, supporting decision-making, and training novice surgeons, ultimately improving patient safety and standardizing procedures. However, data corruption can lead to performance degradation due to issues like occlusion from bleeding or smoke in surgical scenes and problems with data storage and transmission. Therefore, a robust workflow recognition model is urgently needed. In this case, we explore a robust graph-based multimodal approach to integrating vision and kinematic data to enhance accuracy and reliability. Vision data captures dynamic surgical scenes, while kinematic data provides precise movement information, overcoming limitations of visual recognition under adverse conditions. We propose a multimodal Graph Representation network with Adversarial feature Disentanglement (GRAD) for robust surgical workflow recognition in challenging scenarios with domain shifts or corrupted data. Specifically, we introduce a Multimodal Disentanglement Graph Network (MDGNet) that captures fine-grained visual information while explicitly modeling the complex relationships between vision and kinematic embeddings through graph-based message modeling. To align feature spaces across modalities, we propose a Vision-Kinematic Adversarial (VKA) framework that leverages adversarial training to reduce modality gaps and improve feature consistency. Furthermore, we design a Contextual Calibrated Decoder, incorporating temporal and contextual priors to enhance robustness against domain shifts and corrupted data. Extensive comparative and ablation experiments demonstrate the effectiveness of our model and proposed modules. Specifically, we achieved an accuracy of 86.87% and 92.38% on two public datasets, respectively. Moreover, our robustness experiments show that our method effectively handles data corruption during storage and transmission, exhibiting excellent stability and robustness. Our approach aims to advance automated surgi

关键词： Transplantation (surgical)

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Safe Barrier-Constrained control of Uncertain Systems via Event-triggered Learning

arXiv

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arXiv 2024年

作者： Lederer, Armin Begzadić, Azra Hirche, Sandra Cortés, Jorge Herbert, Sylvia The Learning and Adaptive Systems Group Department of Computer Science ETH Zurich Switzerland The Department of Mechanical and Aerospace Engineering University of California San Diego United States The Chair of Information-oriented Control TUM School of Computation Information and Technology Technical University of Munich Germany

While control barrier functions are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel control barrier function-based framework for safe control through event-triggered learning, which switches between prioritizing control performance and improving model accuracy based on the uncertainty of the learned model. By updating a Gaussian process model with training points gathered online, the approach guarantees the feasibility of control barrier function conditions with high probability, such that safety can be ensured in a data-efficient manner. Furthermore, we establish the absence of Zeno behavior in the triggering scheme, and extend the algorithm to sampled-data realizations by accounting for inter-sampling effects. The effectiveness of the proposed approach and theory is demonstrated in simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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Risk-Sensitive Inhibitory control for Safe Reinforcement Learning

Risk-Sensitive Inhibitory Control for Safe Reinforcement Lea...

引用

IEEE Conference on Decision and control

作者： Armin Lederer Erfaun Noorani John S. Baras Sandra Hirche Chair of Information-oriented Control (ITR) School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical and Computer Engineering Institute for Systems Research (ISR) at the University of Maryland College Park MD USA

Humans have the ability to deviate from their natural behavior when necessary, which is a cognitive process called response inhibition. Similar approaches have independently received increasing attention in recent years for ensuring the safety of control. Realized using control barrier functions or predictive safety filters, these approaches can effectively ensure the satisfaction of state constraints through an online adaptation of nominal control laws, e.g., obtained through reinforcement learning. While the focus of these realizations of inhibitory control has been on risk-neutral formulations, human studies have shown a tight link between response inhibition and risk attitude. Inspired by this insight, we propose a flexible, risk-sensitive method for inhibitory control. Our method is based on a risk-aware condition for value functions, which guarantees the satisfaction of state constraints. We propose a method for learning these value functions using common techniques from reinforcement learning and derive sufficient conditions for its success. By enforcing the derived safety conditions online using the learned value function, risk-sensitive inhibitory control is effectively achieved. The effectiveness of the developed control scheme is demonstrated in simulations.

关键词：

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Modulated Model Predictive control for a Four-Leg Indirect Matrix Converter

Modulated Model Predictive Control for a Four-Leg Indirect M...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Mohammad Ali Hosseinzadeh Maryam Sarebanzadeh Ralph Kennel Ebrahim Babaei Marco Rivera Patrick Wheeler Chair of High-Power Converters System Technical University of Munich Munich Germany Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical and Electronic Engineering Faculty of Engineering Chair of Power Electronics Machines and Control (PEMC) Research Group University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Académica Universidad de Talca Talca Chile

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Model Predictive control (MPC) has been increasingly adopted by various converters due to its numerous advantages, including fast dynamic response, multi-objective control, and ease of implementation. However, MPC’s variable switching frequency can degrade waveform quality compared to conventional control techniques that utilize proper modulation methods. To address this issue, combining MPC with a PWM technique allows the converter to operate at a fixed switching frequency, resulting in what is known as modulated model predictive control (M2PC). This study proposes an M2PC for a four-leg converter that is coupled with an indirect matrix converter at the input. The proposed M2PC enhances the output waveform quality using a PWM technique for the four-leg converter while maintaining a low fixed switching frequency. The performance of this approach is demonstrated through the implementation of the proposed M2PC technique in the MATLAB/Simulink environment, showing improvements in waveform quality.

关键词： Switching frequency Switches Pulse width modulation control systems Cost function Prediction algorithms Vectors Matrix converters MATLAB Predictive control

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