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arXiv 2021年

作者： Zhang, Thomas T.C.K. Tu, Stephen Boffi, Nicholas M. Slotine, Jean-Jacques E. Matni, Nikolai Department of Electrical and Systems Engineering University of Pennsylvania United States Google Brain Robotics Courant Institute of Mathematical Sciences New York University United States Nonlinear Systems Laboratory Massachusetts Institute of Technology United States

Motivated by bridging the simulation to reality gap in the context of safety-critical systems, we consider learning adversarially robust stability certificates for unknown nonlinear dynamical systems. In line with approaches from robust control, we consider additive and Lipschitz bounded adversaries that perturb the system dynamics. We show that under suitable assumptions of incremental stability on the underlying system, the statistical cost of learning an adversarial stability certificate is equivalent, up to constant factors, to that of learning a nominal stability certificate. Our results hinge on novel bounds for the Rademacher complexity of the resulting adversarial loss class, which may be of independent interest. To the best of our knowledge, this is the first characterization of sample-complexity bounds when performing adversarial learning over data generated by a dynamical system. We further provide a practical algorithm for approximating the adversarial training algorithm, and validate our findings on a damped pendulum *** Codes 93D05, 93D09 © 2021, CC BY.

关键词： Robust control

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Impedance control for a novel composite modular lower-limb hemiplegic exoskeleton

Impedance control for a novel composite modular lower-limb h...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Bao, Ruoyu Li, Pengbo He, Bailin Wang, Can Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering Chinese University of Hong Kong College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shaanxi Xi'an710021 China

ISBN: (纸本)9781665436786

This paper proposes a control scheme based on impedance control for the smoothing control of the operating movements of a lower-limb hemiplegic exoskeleton robot on the basis of a new composite modular lower-limb hemiplegic ex-oskeleton(CMLHE) structure. The CMLHE has good structural performance and can be used in the rehabilitation of patients with hemiplegia caused by stroke and other diseases. At the same time, the smoothing control by means of impedance control ensures that patients with lower limb hemiplegia can use the CMLHE to achieve smooth walking movements without additional injuries caused by the CMLHE during the rehabilitation training. This paper analyses the performance of this CMLHE robot based on walking movements and demonstrates through kinematic and kinetic modelling and simulation that the impedance control scheme of this CMLHE robot is feasible and effective and can be more widely used in the rehabilitation training of patients with lower limb hemiplegia. © 2021 IEEE.

关键词： Exoskeleton (robotics)

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A modular rehabilitation lower limb exoskeleton for stroke patients with hemiplegia

A modular rehabilitation lower limb exoskeleton for stroke p...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Li, Pengbo Wei, Wenhao Bao, Ruoyu He, Bailin Su, Zhilong Wang, Can Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering The Chinese University of Hong Kong College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shaanxi Xi'an710021 China

ISBN: (纸本)9781665436786

A modular rehabilitation exoskeleton robot for hemiplegic patients (SIAT-H) is created in this paper. The design of the exoskeleton is based on the gait analysis of hemiplegic patients and the movement data of healthy people. Through the motion capture experiments at different speeds, the walking style of patients with hemiplegia is understood. A modular exoskeleton robot, including an independent hip joint, knee joint and ankle joint, is designed. The mechanical structure which is optimized based on the physiological characteristics of human body would not cause additional damage to the joints during walking. In order to ensure the reliability of use, the structural strength is also considered. The kinematics relationship is verified by comparison of simulation data and calculated data. In the end, we made an experimental prototype, and the wearing experiment verified the reliability of the mechanism. © 2021 IEEE.

关键词： Exoskeleton (robotics)

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Low Power and Temperature- Resilient Compute-In-Memory Based on Subthreshold-FeFET

Low Power and Temperature- Resilient Compute-In-Memory Based...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Yifei Zhou Xuchu Huang Jianyi Yang Kai Ni Hussam Amrouch Cheng Zhuo Xunzhao Yin Zhejiang University Hangzhou China ZJU - Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Department of Electrical Engineering University of Notre Dame Notre Dame USA Chair of AI Processor Design Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as “memory wall” issue. Specifically, CiM employing nonvolatile memory (NVM) devices in a crossbar structure can efficiently accelerate multiply-accumulation (MAC) computation, a crucial operator in neural networks among various AI models. Low power CiM designs are thus highly desired for further energy efficiency optimization on AI models. Ferroelectric FET (FeFET), an emerging device, is attractive for building ultra-low power CiM array due to CMOS compatibility, high ION / $I$ O F F ratio, etc. Recent studies have explored FeFET based CiM designs that achieve low power consumption. Nevertheless, subthreshold-operated FeFETs, where the operating voltages are scaled down to the subthreshold region to reduce array power consumption, are particularly vulnerable to temperature drift, leading to accuracy degradation. To address this challenge, we propose a temperature-resilient 2T-1FeFET CiM design that performs MAC operations reliably at subthreahold region from 0°C to 85°C, while consuming ultra-low power. Benchmarked against the VGG neural network architecture running the CIFAR-10 dataset, the proposed 2T1FeFET CiM design achieves 89.45% CIFAR-10 test accuracy. Compared to previous FeFET based CiM designs, it exhibits immunity to temperature drift at an 8-bit wordlength scale, and achieves better energy efficiency with 2866 TOPS/W.

关键词： Semiconductor device modeling Power demand Nonvolatile memory Microprocessors Neural networks Computer architecture Energy efficiency

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Jamming-Assisted Proactive Eavesdropping over Two Suspicious Communication Links

Jamming-Assisted Proactive Eavesdropping over Two Suspicious...

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作者： Zhang, Haiyang Duan, Lingjie Zhang, Rui Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518040 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

This paper studies a new and challenging wireless surveillance problem where a legitimate monitor attempts to eavesdrop two suspicious communication links simultaneously. To facilitate concurrent eavesdropping, our multi-antenna legitimate monitor employs a proactive eavesdropping via jamming approach, by selectively jamming suspicious receivers to lower the transmission rates of the target links. In particular, we are interested in characterizing the achievable eavesdropping rate region for the minimum-mean-squared-error (MMSE) receiver case, by optimizing the legitimate monitor's jamming transmit covariance matrix subject to its power budget. As the monitor cannot hear more than what suspicious links transmit, the achievable eavesdropping rate region is essentially the intersection of the achievable rate region for the two suspicious links and that for the two eavesdropping links. The former region can be purposely altered by the monitor's jamming transmit covariance matrix, whereas the latter region is fixed when the MMSE receiver is employed. Therefore, we first analytically characterize the achievable rate region for the two suspicious links via optimizing the jamming transmit covariance matrix and then obtain the achievable eavesdropping rate region for the MMSE receiver case. In addition, we also consider the MMSE with successive interference cancellation (MMSE-SIC) receiver case and characterize the corresponding achievable eavesdropping rate region by jointly optimizing the time-sharing factor between different decoding orders. Furthermore, extensions to the imperfect channel state information case and the more than two suspicious links scenario are also examined. Finally, numerical results are provided to corroborate our analysis and evaluate the eavesdropping performance. © 2002-2012 IEEE.

关键词： Covariance matrix

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Bayesian Optimization Objective-Based Experimental Design

Bayesian Optimization Objective-Based Experimental Design

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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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Design and Usability of an E-Health Mobile Application 9th

Design and Usability of an E-Health Mobile Application

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9th International Conference on Design, User Experience, and Usability, DUXU 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020

作者： Nogueira, Maria Rita Menezes, Paulo Carvalho, Sérgio Patrão, Bruno Trindade, Inês A. Guiomar, Raquel Duarte, Joana Lapa, Teresa Pinto-Gouveia, José Freitas Castilho, Paula Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department Electrical and Computer Engineering University of Coimbra Coimbra Portugal Centre for Research in Neuropsychology and Cognitive Behavioral Intervention University of Coimbra Coimbra Portugal

ISBN: (纸本)9783030497569

Health applications have increasingly been used to improve physical, mental and social well-being. Chronic pain (CP) is defined as pain that lasts for a period of three months and causes sporadic or constant discomfort. In Portugal, the treatments for this type of pain are almost exclusively pharmacological and with known limited effects. Endowing patients with self-management skills, will help them cope with pain in a more effective way. Psychological treatments (e.g. mindfulness-based interventions) may play a relevant role here, because they intervene on a cognitive, emotional and behavioural level, which in turn helps the chronic pain patient to deal with pain-related disorders and suffering. The current availability of connected and powerful smartphones and tablets creates an opportunity to propose alternative pain management solutions that may be used immediately when pain appears, which has been the argument that favoured the pharmacological solutions. For this we propose a mobile application that guides patients on the mindfulness practice and to self-manage the sensed pain. Learning to gradually adequate pain management may have several advantages such as: reduced the number of consultation visits and consequent waiting lists;increased cost-effectiveness;self-management of chronic pain at the patient’s pace and according to their needs;extend access to the treatment to patients that reside in low density regions. Being an alternative to traditional treatment, the proposed treatment will be under the guidance of qualified health professionals that will supervise treatment sessions and perform the required assessments. By promoting patients’ self-management, the control and monitoring of the chronic pain condition is expected to improve greatly, which in turn may prevent the aggravation of the clinical condition. This research and the mobile application are being developed in a collaboration between the Centre for Research in Neuropsychology and Cognitive Beh

关键词： Cost effectiveness

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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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Optimization of controlled free-time sweeping processes with applications to marine surface vehicle modeling

arXiv

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arXiv 2021年

作者： Cao, Tan H. Khalil, Nathalie T. Mordukhovich, Boris S. Nguyen, Dao Nguyen, Trang Pereira, Fernando Lobo Korea Republic of SYSTEC Faculty of Electrical Engineering Porto University Portugal The Institute for Systems and Robotics Porto4200-465 Portugal Department of Mathematics Wayne State University United States

The paper is devoted to a free-time optimal control problem for sweeping processes. We develop a constructive finite-difference approximation procedure that allows us to establish necessary optimality conditions for discrete optimal solutions and then show how these optimality conditions are applied to solving a controlled marine surface vehicle model. Copyright © 2021, The Authors. All rights reserved.

关键词： Finite difference method

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