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IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS)

作者： Francesco Daghero Gabriele Faraone Michelangelo Grosso Daniele Jahier Pagliari Nicola Di Carolo Giovanna Antonella Franchino Dario Licastro Eugenio Serianni Department of Control and Computer Engineering Politecnico di Torino Turin Italy Analog Power & Discrete MEMS and Sensor R&D Group STMicroelectronics Italy Analog Power & Discrete MEMS and Sensor R&D Group STMicroelectronics Turin Italy

ISBN: (数字)9798350363128

ISBN: (纸本)9798350363135

Analog-on-Top Mixed Signal (AMS) Integrated Circuit (IC) design is a time-consuming process predominantly carried out by hand. Within this flow, usually, some area is reserved by the top-level integrator for the placement of digital blocks. Specific features of the area, such as size and shape, have a relevant impact on the possibility of implementing the digital logic with the required functionality. We present a Machine Learning (ML)-based evaluation methodology for predicting the feasibility of digital implementation using a set of high-level features. This approach aims to avoid time-consuming Place-and-Route trials, enabling rapid feedback between Digital and Analog Back-End designers during top-level placement.

关键词： Integrated circuits Maximum likelihood estimation Shape Design methodology Predictive models Silicon Manufacturing Logic Decision trees

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PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware Object Masking

PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Micha Heiß Jakob Grimm Hansen Dengyun Li Michał Kozłowski Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This paper proposes PredictiveSLAM, a novel extension to ORB-SLAM2, which extracts features from specific regions of interest (ROI). The proposed method was designed with the risk posed both to humans and robotic systems in large-scale industrial sites in mind. The ROI are determined through an object detection network trained to detect moving human beings. The method detects and removes humans from feature extraction, predicting their potential future trajectory. This is done by omitting a specific ROI from extraction, deemed to be occluded in consecutive time steps. Two masking methods -static object and moving object trajectories - are proposed. This approach improves tracking accuracy and the performance of SLAM by removing the dynamic features from the reference for tracking and loop closures. The method is tested on data collected in a laboratory environment and compared against a state-of-the-art ground truth system. The validation data was collected from real-time experiments which aimed at simulating the typical human worker behaviours in industrial environments using an unmanned aerial vehicle (UAV). This study illustrates the advantages of the proposed method over earlier approaches, even with a highly dynamic camera setup on a UAV working in challenging environments.

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Abstracted Model Reduction: A General Framework for Efficient Interconnected System Reduction

arXiv

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arXiv 2024年

作者： Poort, Luuk Janssen, Lars A.L. Besselink, Bart Fey, Rob H.B. van de Wouw, Nathan Department of Mechanical Engineering in the Dynamics & Control group Eindhoven University of Technology Eindhoven5600 MB Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9700 AK Netherlands

This paper introduces the concept of abstracted model reduction: a framework to improve the tractability of structure-preserving methods for the complexity reduction of interconnected system models. To effectively reduce high-order, interconnected models, it is usually not sufficient to consider the subsystems separately. Instead, structure-preserving reduction methods should be employed, which consider the interconnected dynamics to select which subsystem dynamics to retain in reduction. However, structure-preserving methods are often not computationally tractable. To overcome this issue, we propose to connect each subsystem model to a low-order abstraction of its environment to reduce it both effectively and efficiently. By means of a high-fidelity structural-dynamics model from the lithography industry, we show, on the one hand, significantly increased accuracy with respect to standard subsystem reduction and, on the other hand, similar accuracy to direct application of expensive structure-preserving methods, while significantly reducing computational cost. Furthermore, we formulate a systematic approach to automatically determine sufficient abstraction and reduction orders to preserve stability and guarantee a given frequency-dependent error specification. We apply this approach to the lithography equipment use case and show that the environment model can indeed be reduced by over 80% without significant loss in the accuracy of the reduced interconnected model. © 2024, CC BY-NC-ND.

关键词： Structural dynamics

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Interpretable deep temporal neural networks for in-situ monitoring under varying conditions in micro-electrical discharge machining

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Journal of Manufacturing Systems 2025年 81卷 222-237页

作者： Long Ye Cheng Guo Ming Wu Jun Qian Nan Yu Dominiek Reynaerts Institute of Materials and Processes School of Engineering University of Edinburgh Edinburgh EH9 3FB UK Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 PR China Micro & Precision Engineering Group Department of Mechanical Engineering KU Leuven Leuven 3001 Belgium Members Flanders Make Leuven Belgium Department of Computer Science KU Leuven Leuven 3001 Belgium

Monitoring is critical enabler of digitalization in modern manufacturing, supporting enhanced process control, quality assurance, and real-time decision-making. By integrating data-driven techniques with the powerful capabilities of deep learning, monitoring systems can efficiently extract valuable insights from complex, high-dimensional time-series data. However, traditional data-driven approaches often lack interpretability, limiting their adoption in industrial applications that demand high reliability and accountability. To address this challenge, this paper proposes an interpretable monitoring framework based on a deep temporal neural network (DTNN). Designed with a modular architecture, the DTNN integrates key components for embedding, temporal feature learning and classification, enabling it to effectively capture complex underlying patterns in temporal process data and overcome the limitations of conventional methods. The DTNN’s capabilities are demonstrated in the context of micro-electrical discharge machining (micro-EDM), a prominent non-traditional machining process known for producing intricate and high-precision components. Through a pulse discrimination task utilizing a large dataset of reliable labels, the DTNN achieves superior classification accuracy under varying processing parameters while providing interpretable insights into discharge phenomena. Furthermore, the DTNN monitoring approach is applied to a deep-hole drilling process in micro-EDM, enabling closed-loop control of discharge status and ensuring long-term process stability. The DTNN’s modular design, interpretability and real-time adaptability underscore its potential for advancing data-driven monitoring systems in digital manufacturing.

关键词： CNN Deep learning EDM Interpretable LSTM Process control Process monitoring Signal processing

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Composite DC-DC Converter with Bipolar/bidirectional Current-Fed Push-pull Voltage Regulator 16

Composite DC-DC Converter with Bipolar/bidirectional Current...

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16th Brazilian Power Electronics Conference, COBEP 2021

作者： Klein Faistel, Tiago Miguel Jank, Henrique Ribeiro, Gustavo Colpes Da Silva Martins, Mario Lucio Correa Lopes, Luiz Antonio Power Electronics and Control Research Group Federal University of Santa Maria Santa Maria Brazil Concordia University Department of Electrical and Computer Engineering Montreal Canada

ISBN: (纸本)9781665421294

This paper presents a power converter to interface an extra low voltage bus, ELV (48 V) and a low voltage bus, LV (380 V) in a residential dc-dc nanogrid of 1.2 kW. The proposed converter makes use of a composite structure where a DC transformer (DCX converter) handles the major amount of the processed power between the DC buses. DCX is design to operate at a fixed duty-cycle where the transformer turns-ratio is optimized to ensure the lowest conduction losses. To provide voltage regulation to ELV bus, a parallel voltage regulator (DCR) is employed. The power handled by the DCR is proportional to its input voltage across capacitor $C$ 1.1 Results from a 1.2 kW laboratory prototype confirm the feasibility of the proposed composite converter and reveals that it is a strong candidate for DC-DC applictations with a narrow volage regulation range. © 2021 IEEE.

关键词： Voltage regulators

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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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Model Predictive control for Collision-free Spacecraft Formation with Artificial Potential Functions

Model Predictive Control for Collision-free Spacecraft Forma...

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Mediterranean Conference on control and Automation (MED)

作者： Danilo Menegatti Alessandro Giuseppi Antonio Pietrabissa Department of Computer Control and Management Engineering "Antonio Ruberti" of the University of Rome "La Sapienza" Rome Italy Space Control Group of CRAT Rome Italy

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

A collision-free formation control strategy for flying in formation is presented. A linear control law is developed by means of Model Predictive control (MPC) via the dual-mode paradigm [1]. Collision avoidance is dealt with by using Artificial Potential Functions (APFs) to keep a desired safe distance from the obstacles. The main innovation in the proposed approach is that each spacecraft independently performs the collision avoidance manoeuvres and, as a consequence, the APFs-based collision avoidance control is in charge also of the collision avoidance between two spacecraft. The optimality of the solution is discussed and numerical simulations show the effectiveness of the proposed method.

关键词： Space vehicles Technological innovation Tracking System dynamics Space debris Numerical simulation Mathematical models

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Towards reliable data-based optimal and predictive control using extended DMD

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IFAC-PapersOnLine 2023年第1期56卷 169-174页

作者： Manuel Schaller Karl Worthmann Friedrich Philipp Sebastian Peitz Feliks Nüske Technische Universität Ilmemau Institute of Mathematics Optimization-based Control group Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

While Koopman-based techniques like extended Dynamic Mode Decomposition are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To this end, both sources of error resulting from a finite dictionary and only finitely-many data points in the generation of the surrogate model have to be taken into account. We generalize the rigorous analysis of the approximation error to the control setting while simultaneously reducing the impact of the curse of dimensionality by using a recently proposed bilinear approach. In particular, we establish uniform bounds on the approximation error of state-dependent quantities like constraints or a performance index enabling data-based optimal and predictive control with guarantees.

关键词： Approximation error data-based dictionary size eDMD estimation error finite data Koopman predicted control projection error optimal control

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Safe Reinforcement Learning via Confidence-Based Filters

arXiv

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arXiv 2022年

作者： Curi, Sebastian Lederer, Armin Hirche, Sandra Krause, Andreas Learning & Adaptive Systems Group Department of Computer Science ETH Zurich Switzerland Information-oriented Control Department of Electrical and Computer Engineering Technical University of Munich Germany

— 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. Finally, 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. © 2022, CC BY.

关键词： Reinforcement learning

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Autonomous Vehicle Drift With a Soft Actor-critic Reinforcement Learning Agent 20

Autonomous Vehicle Drift With a Soft Actor-critic Reinforcem...

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20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Hunor Toth, Szilard Janos Viharos, Zsolt Bardos, Adam Institute of Computer Science and Control Research Laboratory on Engineering Management Intelligence Intelligent Processes Research Group Budapest Hungary John von Neumann University Faculty of Economics Kecskemét Hungary Budapest University of Technology and Economics Department of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665497046

Self-driving vehicles have become a more and more important field in recent years. Supported by the techniques of Artificial Intelligence (AI), the current tendency of positive results in applications is making it a promising area to focus further research. Additionally, Reinforcement Learning (RL) is already proved to be an efficient approach for complex problems, e.g. robots, industrial systems and also games (like chess, Go), *** is a driving technique at handling limits where the driver intentionally oversteers, with loss of traction, while driving the vehicle through the entirety of a corner. It is a very challenging control task and often results in an accident when it occurs on public roads, consequently, the efficient control of this motion is especially important in the safety of autonomous *** paper reports novel research results whose main goal is to develop a self-driving agent for drift motion control based on vehicle simulation by Matlab/Simulink. Longitudinal and lateral velocity together with the yaw rate formed the state representation of the vehicle. The agent action space consists of two continuous actuator values: pedal ratio and roadwheel angle. The goal of the agent is twofold: first, it has to jump into a drifting state, second, it has to keep the vehicle in *** simulation results show that the proposed Soft Actor-Critic (SAC) RL agent is capable of learning to approach a pre-determined drift equilibrium from cornering and staying in this drift situation as well. For the training, the solution excluded using any kind of prior data, it only works with information gained from the simulation model, which is a remarkable difference from the actual state-of-the-art RL-based solutions. © 2022 IEEE.

关键词： Training Roads Simulation Reinforcement learning Mathematical models Data models Artificial intelligence

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