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American Control Conference

作者： Mahdi Imani Seyede Fatemeh Ghoreishi Department of Electrical and Computer Engineering George Washington University Institute for Systems Research (ISR) University of Maryland

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Design has become a salient part of most of the scientific and engineering tasks, embracing a wide range of domains including real experimental settings (e.g., material discovery or drug design), simulation-based design, and hyperparameter tuning. Model-based experimental design refers to a broad class of techniques, applicable to domains that a partial knowledge about the underlying process exists. Unlike entropy- based techniques which aim to reduce the whole uncertainty in the process, the mean objective cost of uncertainty (MOCU) is a rigorous statistically-oriented experimental design framework which takes the main objective into account during the decision making. However, the lack of scalability of this framework has restricted its application to domains with very small design spaces. This paper proposes a framework using the combination of Bayesian optimization and MOCU policy, which enables experimental design to much larger design spaces and systems. The reliability, scalability and efficiency of the proposed framework are investigated through experimental design for optimal structural intervention in gene regulatory networks.

关键词： domains Frameworks Scalability Gene Regulatory Networks Bayesian inference Phyla

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Bayesian Optimization for Efficient Design of Uncertain Coupled Multidisciplinary systems

Bayesian Optimization for Efficient Design of Uncertain Coup...

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American Control Conference

作者： Seyede Fatemeh Ghoreishi Mahdi Imani Institute for Systems Research (ISR) University of Maryland Department of Electrical and Computer Engineering George Washington University

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Stabilization of complex cyber-physical systems is extremely important in keeping the critical infrastructure and the environment safe. This is, in particular, critical in coupled multidisciplinary systems with several subsystems interacting with each other in an uncertain environment. The design of stabilized complex systems depends on a proper set of inputs to these subsystems, in such a way that the best stationary behavior of these systems is achieved. Despite several attempts for stabilizing the coupled multidisciplinary systems, the existing techniques still have their critical limitations and issues due to the unrealistic deterministic assumption in some cases as well as inability in handling large-scale systems. In this paper, we introduce a Bayesian framework using the combination of Bayesian optimization technique and Gibbs sampling method, which enables scalable, efficient and fast learning of the best input to achieve the best design of multidisciplinary systems. The accuracy and speed of the proposed framework will be demonstrated in numerical experiments using an aerodynamics- structures system and a mathematical example.

关键词： SUB-SYSTEM Multidisciplinary Bayesian inference Plant uncertain Cyber-physical systems system behavior

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Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization

Neural-iLQR: A Learning-Aided Shooting Method for Trajectory...

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IEEE International Conference on robotics and Biomimetics

作者： Zilong Cheng Yulin Li Kai Chen Jianghua Duan Jun Ma Tong Heng Lee Department of Electrical and Computer Engineering National University of Singapore Singapore Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong SAR China Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong SAR China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooting method, it relies heavily on an accurate system model to update the optimal control actions and the trajectory determined with forward integration, thus becoming vulnerable to inevitable model inaccuracies. Recently, substantial research efforts in learning-based methods for optimal control problems have been progressing significantly in addressing unknown system models, particularly when the system has complex interactions with the environment. Yet an incremental machine learning method or a deep neural network is normally required to fit a substantial scale of sampling motion data. Existing works mainly focus on capturing the whole picture of the motion pattern from trial data (which is essentially noisy and hard to identify with any simple predefined structures) for complex robot systems. Instead, we present Neural-iLQR, a learning-aided shooting method over the unconstrained control space, in which a neural network with a simple structure is used to drive the optimization process towards optimal direction. Through comprehensive evaluations on two illustrative control tasks using three different kinds of lightweight network structures, the proposed method is shown to outperform the conventional iLQR significantly with fast and continuous cost convergence in the presence of inaccuracies in system models, which demonstrates the generalizability and robustness of the proposed learning-aided method.

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Physiological assessment of brain, cardiovascular, and respiratory changes in multimodal motor imagery brain-computer interface training

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Brain-computer Interfaces 2025年第1期12卷

作者： Evangelos Georgaras Athanasios Vourvopoulos a Electrical and Computer Engineering Department National Technical University of Athens Athens Greece b Bioengineering Department Institute for Systems and Robotics - Lisboa Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal

ABSTRACTBrain-computer Interfaces (BCIs) can provide a non-muscular communication channel for individuals with motor impairments. When integrated with virtual reality (VR) and haptic feedback, motor imagery (MI)-based BCIs can augment the rehabilitation outcome for patients with severe motor impairments. However, the physiological impact of these protocols beyond brain-related signals, that reflect autonomic nervous system (ANS) activity, remains underexplored. This study aims to investigate variations in a broader range of physiological signals besides electroencephalography (EEG) – including electrocardiography (ECG), photoplethysmography (PPG), and respiration – across different experimental conditions and to identify the factors driving these changes. 19 healthy subjects underwent MI training across five combinations of feedback conditions: abstract vs. realistic feedback, head-mounted display (HMD) vs. monitor, and the presence or absence of haptic feedback, compared with motor execution data. PPG results were compared with ECG results to assess the reliability of the finger-clip PPG sensor regarding its ability to replace ECG in cases where ease of use and unobtrusiveness in heart monitoring are required. Current findings show that VR-based MI with haptic feedback, results in increased modulation of Beta and Gamma bands, while all conditions may impose a greater mental burden than motor execution, as indicated by the increased respiration rate and decreased heart-rate variability.

关键词： Brain-computer interfaces autonomic nervous system motor imagery virtual reality haptic feedback

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Study of the Equivalent Impedance of a Resonator Array Using Difference Equations 4th

Study of the Equivalent Impedance of a Resonator Array Using...

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4th International Conference on Differential and Difference Equations with Applications, ICDDEA 2019

作者： Albuquerque, Helena Alberto, José CMUC Departamento de Matemática Universidade de Coimbra Apartado 3008 Coimbra3001-454 Portugal Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3030-290 Portugal

ISBN: (纸本)9783030563226

In this paper we present a mathematical model, based on the theory of difference equations, to study the wireless power transfer using resonator arrays. This approach is relevant for a better understanding of the system because it gives us a closed form for the equivalent impedance of the array of resonators that allows us to make predictions about the behavior of the system. We prove that the complete solution of the problem depends only on the initial conditions and possible perturbations introduced. Using the computer software MATLAB we present in the last section of this paper some illustrations of our results. © 2020, Springer Nature Switzerland AG.

关键词： Difference equations

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Hydrothermally Synthesis of Binary Niznfe2o4-Cnt Nanocomposite for Energy Storage Application

SSRN

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SSRN 2023年

作者： Ghadimi, Laleh Saleh Tabrizi, Amin Goljanian Rezaie, Ehsan Parvini, Elahe Tavakoli, Mahmoud Abouzari-Lotf, Ebrahim Hajalilou, Abdollah Arsalani, Nasser Pahlevani, Majid Department of Electrical and Computer Engineering Queen's University KingstonON Canada Research Laboratory of Polymer Department of Organic and Biochemistry Faculty of Chemistry University of Tabriz Iran Laboratory for Advanced Materials Faculty of Natural Sciences Comenius University Bratislava842 15 Slovakia Soft and Printed Microelectronics Laboratory Institute of Systems and Robotics University of Coimbra Coimbra Portugal Electrochemical Energy Storage Helmholtzstraße 11 UlmD-89081 Germany

Our research aimed to enhance carbon-based electrochemical double-layer capacitors (EDLCs) by incorporating faradic redox processes. We synthesized a Ni0.5Zn0.5Fe2O4-Carbon Nanotube (NZF-CNT) nanocomposite via hydrothermal methods at various pH levels. X-ray diffraction (XRD) analysis at pH 12 confirmed the formation of spinel nickel-zinc ferrite with 15 nm particles. X-ray photoelectron spectroscopy (XPS) verified cation distribution changes, and high-resolution transmission electron microscopy (HRTEM) revealed well-arranged NZF crystals around CNTs. The successful fusion of NZF and CNTs into the nanocomposite framework was demonstrated. NZF-CNT exhibited a remarkable specific capacitance of approximately 368 F/g at 1.25 A/g, surpassing pure ferrite's 110 F/g. Even after 5000 charge/discharge cycles at 1 A/g, it retained 88.5% of its initial capacitance. Our findings showcase the potential of NZF-CNT as a promising material for energy storage devices, offering a unique combination of EDLC and pseudo-capacitive properties. This approach opens new avenues for improving supercapacitor performance and their diverse applications. © 2023, The Authors. All rights reserved.

关键词： Supercapacitor

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Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

arXiv

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： robotics

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Symbolic Models for Interconnected Impulsive systems

Symbolic Models for Interconnected Impulsive Systems

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IEEE Conference on Decision and Control

作者： Sadek Belamfedel Alaoui Adnane Saoud Pushpak Jagtap Abdalla Swikir School of Computer Science at Mohammed VI Polytechnical University Benguerir Morocco Centrale-Supelec University Paris-Saclay Gif-sur-Yvette France Robert Bosch Center for Cyber-Physical Systems Indian Institute of Science Bangalore Chair of Robotics and Systems Intelligence and MIRMI Technical University Munich (TUM) Munich Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Albaida Libya Centre for Tactile Internet with Human-in-the-Loop (CeTI) Dresden Germany

In this paper, we present a compositional method-ology for constructing symbolic models of nonlinear interconnected impulsive systems. Our approach relies on the concept of “alternating simulation function” to establish a relation-ship between concrete subsystems and their symbolic models. Assuming some small-gain type conditions, we develop an alternating simulation function between the symbolic models of individual subsystems and those of the nonlinear interconnected impulsive systems. To construct symbolic models of nonlinear impulsive subsystems, we propose an approach that depends on incremental input-to-state stability and forward completeness properties. Finally, we demonstrate the advantages of our framework through a case study.

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Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Simultaneously Calibration of Multi Hand-Eye Robot System Based on Graph

arXiv

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arXiv 2023年

作者： Zhou, Zishun Ma, Liping Liu, Xilong Cao, Zhiqiang Yu, Junzhi The Institute of Automation Chinese Academy of Sciences Beijing100190 China The College of Electrical Engineering and Computer Science Australian National University Canberra2601 Australia The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

Precise calibration is the basis for the vision-guided robot system to achieve high-precision operations. systems with multiple eyes (cameras) and multiple hands (robots) are particularly sensitive to calibration errors, such as micro-assembly systems. Most existing methods focus on the calibration of a single unit of the whole system, such as poses between hand and eye, or between two hands. These methods can be used to determine the relative pose between each unit, but the serialized incremental calibration strategy cannot avoid the problem of error accumulation in a large-scale system. Instead of focusing on a single unit, this paper models the multi-eye and multi-hand system calibration problem as a graph and proposes a method based on the minimum spanning tree and graph optimization. This method can automatically plan the serialized optimal calibration strategy in accordance with the system settings to get coarse calibration results initially. Then, with these initial values, the closed-loop constraints are introduced to carry out global optimization. Simulation experiments demonstrate the performance of the proposed algorithm under different noises and various hand-eye configurations. In addition, experiments on real robot systems are presented to further verify the proposed method. © 2023, CC BY-SA.

关键词： Multipurpose robots

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