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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Kejun Li Jeeseop Kim Xiaobin Xiong Kaveh Akbari Hamed Yisong Yue Aaron D. Ames Department of Computation and Neural Systems Caltech Pasadena CA USA Department of Mechanical and Civil Engineering Caltech Pasadena CA USA Department of Mechanical Engineering University of Wisconsin-Madison Madison WI USA Department of Mechanical Engineering Virginia Tech VA USA Department of Computing and Mathematical Sciences Caltech Pasadena CA USA Department of Control and Dynamical Systems Caltech Pasadena CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

关键词： Adaptation models Analytical models Perturbation methods Exoskeletons Predictive models Hardware Trajectory Numerical models Predictive control Payloads

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Hierarchical Event-Triggered systems: Safe Learning of Quasi-Optimal Deadline Policies

Hierarchical Event-Triggered Systems: Safe Learning of Quasi...

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IEEE Conference on Decision and control

作者： Pio Ong Manuel Mazo Aaron D. Ames Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA Delft Center for Systems and Control Faculty of Mechanical Engineering Delft University of Technology The Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We present a hierarchical architecture to improve the efficiency of event-triggered control (ETC) in reducing resource consumption. This paper considers event-triggered systems generally as an impulsive control system in which the objective is to minimize the number of impulses. Our architecture recognizes that traditional ETC is a greedy strategy towards optimizing average inter-event times and introduces the idea of a deadline policy for the optimization of long-term discounted inter-event times. A lower layer is designed employing event-triggered control to guarantee the satisfaction of control objectives, while a higher layer implements a deadline policy designed with reinforcement learning to improve the discounted inter-event time. We apply this scheme to the control of an orbiting spacecraft, showing superior performance in terms of actuation frequency reduction with respect to a standard (one-layer) ETC while maintaining safety guarantees.

关键词： Space vehicles Event detection Transfer learning Optimal control Stochastic processes Reinforcement learning Trajectory Safety Optimization Standards

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Stochastic Online Optimization for Cyber-Physical and Robotic systems

arXiv

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arXiv 2024年

作者： Ma, Hao Zeilinger, Melanie Muehlebach, Michael Learning and Dynamical Systems Group Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen Germany Department of Mechanical and Process Engineering Institute for Dynamic Systems and Control Leonhardstrasse 21 Zürich8092 Switzerland

We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic systems. Our problem formulation accommodates constraints that model the evolution of a cyber-physical system, which has, in general, a continuous state and action space, is nonlinear, and where the state is only partially observed. We also incorporate an approximate model of the dynamics as prior knowledge into the learning process and show that even rough estimates of the dynamics can significantly improve the convergence of our algorithms. Our online optimization framework encompasses both gradient descent and quasi-Newton methods, and we provide a unified convergence analysis of our algorithms in a non-convex setting. We also characterize the impact of modeling errors in the system dynamics on the convergence rate of the algorithms. Finally, we evaluate our algorithms in simulations of a flexible beam, a four-legged walking robot, and in real-world experiments with a ping-pong playing robot. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Dynamic Walking on Highly Underactuated Point Foot Humanoids: Closing the Loop between HZD and HLIP

arXiv

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arXiv 2024年

作者： Ghansah, Adrian B. Kim, Jeeseop Li, Kejun Ames, Aaron D. Department of Control and Dynamical Systems California Institute of Technology PasadenaCA91125 United States Department of Mechanical and Civil Engineering California Institute of Technology PasadenaCA91125 United States Department of Computation and Neural Systems California Institute of Technology PasadenaCA91125 United States

Realizing bipedal locomotion on humanoid robots with point feet is especially challenging due to their highly underactuated nature, high degrees of freedom, and hybrid dynamics resulting from impacts. With the goal of addressing this challenging problem, this paper develops a control framework for realizing dynamic locomotion and implements it on a novel point foot humanoid: ADAM. To this end, we close the loop between Hybrid Zero Dynamics (HZD) and Hybrid linear inverted pendulum (HLIP) based step length regulation. To leverage the full-order hybrid dynamics of the robot, walking gaits are first generated offline by utilizing HZD. These trajectories are stabilized online through the use of a HLIP based regulator. Finally, the planned trajectories are mapped into the full-order system using a task space controller incorporating inverse kinematics. The proposed method is verified through numerical simulations and hardware experiments on the humanoid robot ADAM marking the first humanoid point foot walking. Moreover, we experimentally demonstrate the robustness of the realized walking via the ability to track a desired reference speed, robustness to pushes, and locomotion on uneven terrain. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverted pendulum

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Hierarchical Event-Triggered systems: Safe Learning of Quasi-Optimal Deadline Policies

arXiv

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arXiv 2024年

作者： Ong, Pio Mazo, Manuel Ames, Aaron D. The Department of Mechanical and Civil Engineering California Institute of Technology PasadenaCA91125 United States The Delft Center for Systems and Control Faculty of Mechanical Engineering Delft University of Technology Netherlands

关键词： Orbits

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Automated Generation of Hydraulic Models and its use for Sensitivity Analysis: Response Time Variation with Channel Parameters

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IFAC-PapersOnLine 2024年第2期58卷 55-60页

作者： Ronald van Nooijen Alla Kolechkina Water Management Department Faculty of Civil Engineering and Geosciences Delft University of Technology Delft Netherlands Delft Center for Systems and Control Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Delft Netherlands

A first prototype of a Python package that can generate input for, perform runs of, and the analyse output of the Delft3D FM 1D2D simulation software corresponding to both simple and complex canal networks is demonstrated. The package can, for instance, generate input files for a canal system with a wide range of friction values and a range of variations on the design cross sections and structures used. It can then start parallel Delft3D runs for these input files. Finally, results can be analysed from Python. Results are presented for the rise time associated with a step change in the discharge for a prismatic canal with a trapezoidal cross section and either a weir or a gate at the downstream end. The size of the step change, the parameters of the cross section, the friction, and the canal slope are varied.

关键词： Modeling identification of environmental systems Real time control of environmental systems Hydroinformatics Open channel flow Weir Gate

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Deep-Learning-based damage detection of an experimentally tested full-scale wind turbine blade.

e-Journal of Nondestructive Testing

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e-Journal of Nondestructive Testing 2024年第7期29卷

作者： Varouxis, Theodoros Dertimanis, Vasilis Abdallah, Imad Chatzi, Eleni Pakrashi, Vikram Malekjafarian, Abdollah Structural Dynamics and Assessment Laboratory School of Civil Engineering University College Dublin Dublin Ireland Department of Civil Environmental and Geomatic Engineering ETH Zurich Zurich Switzerland UCD Centre for Mechanics Dynamical Systems and Risk Laboratory School of Mechanical and Materials Engineering University College Dublin Dublin Ireland

Reducing the operation and maintenance (O&M) costs of wind farms can foster adoption of environmentally sustainable and cost-effective energy sources. O&M costs in Wind Turbines can account up to 35% of the turbine’s total levelized cost per KWh, with a large portion attributed to that for wind turbine blades (WTB). In the present study, an experimental campaign for fatigue damage progression monitoring is presented. After subjecting the WTB to cyclic loading, an artificial transverse crack was introduced in the trailing edge (TE).The dynamic response of the blade to three different excitation schemes-white noise, white noise with the inclusion of a fundamental sinusoidal component and a frequency sweep-is obtained via the use of accelerometers, strain gauges and a real-time deflection monitoring system. These tests yielded generated 4 different health states, the initial healthy and 3 damaged states. The progressive damage is presented using frequency response functions (FRFs),while a simple deep-learning damage detection framework is developed. A conventional autoencoder (AE) is trained using the Frequency Response Functions (FRFs) corresponding to the blade’s healthy state;then, an unsupervised damage detection algorithm is formulated using the AE. © 2024, NDT. net GmbH and Co. KG. All rights reserved.

关键词： Damage detection

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Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion

arXiv

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arXiv 2023年

作者： Tucker, Maegan Li, Kejun Ames, Aaron D. The School of Electrical and Computer Engineering The Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30308 United States The Department of Computation and Neural Systems California Institute of Technology PasadenaCA91125 United States The Department of Mechanical and Civil Engineering The Department of Control and Dynamical Systems California Institute of Technology PasadenaCA91125 United States

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness - the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multicontact walking on the Atalante lower-body exoskeleton. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Instrumenting an Operational Train for Continuous Monitoring of Bridges and Track 1st

Instrumenting an Operational Train for Continuous Monitoring...

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1st Conference of the European Association on Quality control of Bridges and Structures, EUROSTRUCT 2021

作者： Micu, E. Alexandra Obrien, Eugene Bowe, Cathal Okosun, Favour Osose Morgan, David Pakrashi, Vikram Dynamical Systems and Risk Laboratory School of Mechanical and Materials Engineering University College Dublin Dublin Ireland School of Civil Engineering University College Dublin Dublin Ireland Iarnród Éireann Dublin Ireland Science Foundation Ireland MaREI Centre University College Dublin Dublin Ireland

ISBN: (纸本)9783030918767

A well-designed data acquisition (DAQ) system is essential to collect accurate (bridge and track) data over a long-term period. Direct instrumentation of a single component of the network (e.g. sensors installed on a bridge) is costly and offers minimal data. An instrumented train that operates for a long period of time over the entire network can collect a large amount of data at less expense and maintenance of the system. A measurement system installed on an in-service train for monitoring the Irish Rail Network for a duration of twelve months is proposed here to monitor the track and bridge condition. Uni-axial and tri-axial accelerometers are proposed to instrument sprung and un-sprung masses to measure vibration in the vertical direction and along the three axes. The vibration data are transmitted to and stored in the data logger. A high accuracy GPS system is used for referencing the data to geographical coordinates. An existing power supply on the train powers the data acquisition system. Such instrumentation is fundamental to obtaining information around risk assessment and performance evaluation of critical infrastructures, especially due to their inherent uncertainties. With a system like that described in this paper, investigation into these uncertain systems can be carried out in a systematics manner and without having to close the operations of the structure. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Accelerometers

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基于反常热传导的分数阶热弹耦合理论

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Acta Mechanica Sinica 2024年第5期40卷 141-149页

作者：尉亚军吴华邓子辰 School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an 710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an 710072 China State Key Laboratory for Strength and Vibration of Mechanical Structures Xi’an Jiaotong University Xi’an 710049 China

原子模拟和实验研究表明低维纳米材料具有反常热传导特性,建立唯象反常热弹耦合模型,研究强热传导导致的热力耦合响应,对微纳器件的安全稳定运行至关重要.本文旨在从反常热传导的视角重新审视分数阶热弹耦合模型。反常热传导由第二声效... 详细信息

原子模拟和实验研究表明低维纳米材料具有反常热传导特性,建立唯象反常热弹耦合模型,研究强热传导导致的热力耦合响应,对微纳器件的安全稳定运行至关重要.本文旨在从反常热传导的视角重新审视分数阶热弹耦合模型。反常热传导由第二声效应引起,考虑波动热传导理论,建立了波动热传导与粘弹性理论的类比,阐明了Cattaneo-Vernotte模型和Green-Naghdi模型的关联.通过引入分数阶导数,构建了基于Cattaneo-Vernotte和Green-Naghdi理论的分数阶热弹耦合模型。数值结果表明:对分数阶次区间[O,1],分数阶Cattaneo-Vernotte(FCV)I模型和分数阶Green-Naghdi(FGN)I-III模型都能预测反常热弹耦合响应:比经典热弹性理论更高的温度和应力;温度和应力随时间的变化显示:FGNII模型在所有时间范围内都能得到反常响应.对Green-Naghdi模型及其分数阶模型的进一步系统研究,有助于揭示反常热传导和热弹耦合机理,并促进纳米材料的广泛应用.

关键词：粘弹性理论分数阶导数热弹耦合热弹性理论热传导低维纳米材料分数阶模型微纳器件

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