IEEE open journal of control systems最新文献_第10页

Finite Sample Analysis of Minmax Variant of Offline Reinforcement Learning for General MDPs 一般MDP离线强化学习Minmax变量的有限样本分析

IEEE open journal of control systems Pub Date : 2022-08-16 DOI: 10.1109/OJCSYS.2022.3198660

Jayanth Reddy Regatti;Abhishek Gupta

引用次数: 0

Optimal Network Interventions to Control the Spreading of Oscillations 控制振荡传播的最优网络干预

IEEE open journal of control systems Pub Date : 2022-08-10 DOI: 10.1109/OJCSYS.2022.3193127

Ahmed Allibhoy;Federico Celi;Fabio Pasqualetti;Jorge Cortés

引用次数: 4

DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting DeepSplit:通过算子分裂对深度神经网络进行可扩展验证

IEEE open journal of control systems Pub Date : 2022-06-30 DOI: 10.1109/OJCSYS.2022.3187429

Shaoru Chen;Eric Wong;J. Zico Kolter;Mahyar Fazlyab

引用次数: 11

Dissipative Deep Neural Dynamical Systems 耗散深度神经动力学系统

IEEE open journal of control systems Pub Date : 2022-06-28 DOI: 10.1109/OJCSYS.2022.3186838

Ján Drgoňa;Aaron Tuor;Soumya Vasisht;Draguna Vrabie

引用次数: 4

A Discrete Fractional Order Adaptive Law for Parameter Estimation and Adaptive Control 用于参数估计和自适应控制的离散分数阶自适应律

IEEE open journal of control systems Pub Date : 2022-06-21 DOI: 10.1109/OJCSYS.2022.3185002

Mohamed Aburakhis;Raúl Ordóñez;Ouboti Djaneye-Boundjou

引用次数: 0

Learning Lipschitz Feedback Policies From Expert Demonstrations: Closed-Loop Guarantees, Robustness and Generalization 从专家演示中学习Lipschitz反馈策略：闭环保证、鲁棒性和泛化

IEEE open journal of control systems Pub Date : 2022-06-17 DOI: 10.1109/OJCSYS.2022.3181584

Abed AlRahman Al Makdah;Vishaal Krishnan;Fabio Pasqualetti

{"title":"Learning Lipschitz Feedback Policies From Expert Demonstrations: Closed-Loop Guarantees, Robustness and Generalization","authors":"Abed AlRahman Al Makdah;Vishaal Krishnan;Fabio Pasqualetti","doi":"10.1109/OJCSYS.2022.3181584","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3181584","url":null,"abstract":"In this work, we propose a framework in which we use a Lipschitz-constrained loss minimization scheme to learn feedback control policies with guarantees on closed-loop stability, adversarial robustness, and generalization. These policies are learned directly from expert demonstrations, contained in a dataset of state-control input pairs, without any prior knowledge of the task and system model. Our analysis exploits the Lipschitz property of the learned policies to obtain closed-loop guarantees on stability, adversarial robustness, and generalization over scenarios unexplored by the expert. In particular, first, we establish robust closed-loop stability under the learned control policy, where we provide guarantees that the closed-loop trajectory under the learned policy stays within a bounded region around the expert trajectory and converges asymptotically to a bounded region around the origin. Second, we derive bounds on the closed-loop regret with respect to the expert policy and on the deterioration of the closed-loop performance under bounded (adversarial) disturbances to the state measurements. These bounds provide certificates for closed-loop performance and adversarial robustness for learned policies. Third, we derive a (probabilistic) bound on generalization error for the learned policies. Numerical results validate our analysis and demonstrate the effectiveness of our robust feedback policy learning framework. Finally, our results support the existence of a potential tradeoff between nominal closed-loop performance and adversarial robustness, and that improvements in nominal closed-loop performance can only be made at the expense of robustness to adversarial perturbations.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"85-99"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09798865.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50349021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Generalized Lyapunov Demodulator: High-Bandwidth, Low-Noise Amplitude and Phase Estimation 广义李雅普诺夫解调器：高带宽、低噪声幅度和相位估计

IEEE open journal of control systems Pub Date : 2022-06-08 DOI: 10.1109/OJCSYS.2022.3181111

Michael R. P. Ragazzon;Saverio Messineo;Jan Tommy Gravdahl;David M. Harcombe;Michael G. Ruppert

{"title":"The Generalized Lyapunov Demodulator: High-Bandwidth, Low-Noise Amplitude and Phase Estimation","authors":"Michael R. P. Ragazzon;Saverio Messineo;Jan Tommy Gravdahl;David M. Harcombe;Michael G. Ruppert","doi":"10.1109/OJCSYS.2022.3181111","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3181111","url":null,"abstract":"Effective demodulation of amplitude and phase is a requirement in a wide array of applications. Recent efforts have increased the demodulation performance, in particular, the Lyapunov demodulator allows bandwidths up to the carrier frequency of the signal. However, being inherently restricted to first-order filtering of the input signal, it is highly sensitive to frequency components outside its passband region. This makes it unsuitable for certain applications such as multifrequency atomic force microscopy (AFM). In this article, the structure of the Lyapunov demodulator is transformed to an equivalent form and generalized by exploiting the internal model principle. The resulting generalized Lyapunov demodulator structure allows for arbitrary filtering order and is easy to implement, requiring only a bandpass filter, a single integrator, and two nonlinear transformations. The generalized Lyapunov demodulator is implemented experimentally on a field-programmable gate array (FPGA). Then it is used for imaging in an AFM and benchmarked against the standard Lyapunov demodulator and the widely used lock-in amplifier. The lock-in amplifier achieves great noise attenuation capabilities and off-mode rejection at low bandwidths, whereas the standard Lyapunov demodulator is shown to be effective at high bandwidths. We demonstrate that the proposed demodulator combines the best from the two state-of-the-art demodulators, demonstrating high bandwidths, large off-mode rejection, and excellent noise attenuation simultaneously.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"69-84"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09790310.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50237536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Noninvasive Breathing Effort Estimation of Mechanically Ventilated Patients Using Sparse Optimization 稀疏优化法估计机械通气患者的无创呼吸力

IEEE open journal of control systems Pub Date : 2022-06-03 DOI: 10.1109/OJCSYS.2022.3180002

Joey Reinders;Bram Hunnekens;Nathan van de Wouw;Tom Oomen

引用次数: 2

Optimally Biomimetic Passivity-Based Control of a Lower-Limb Exoskeleton Over the Primary Activities of Daily Life 基于最佳仿生被动性的下肢外骨骼对日常生活主要活动的控制

IEEE open journal of control systems Pub Date : 2022-04-12 DOI: 10.1109/OJCSYS.2022.3165733

Jianping Lin;Nikhil V. Divekar;Gray C. Thomas;Robert D. Gregg

{"title":"Optimally Biomimetic Passivity-Based Control of a Lower-Limb Exoskeleton Over the Primary Activities of Daily Life","authors":"Jianping Lin;Nikhil V. Divekar;Gray C. Thomas;Robert D. Gregg","doi":"10.1109/OJCSYS.2022.3165733","DOIUrl":"https://doi.org/10.1109/OJCSYS.2022.3165733","url":null,"abstract":"Task-specific, trajectory-based control methods commonly used in exoskeletons may be appropriate for individuals with paraplegia, but they overly constrain the volitional motion of individuals with remnant voluntary ability (representing a far larger population). Human-exoskeleton systems can be represented in the form of the Euler-Lagrange equations or, equivalently, the port-controlled Hamiltonian equations to design control laws that provide \u0000<italic>task-invariant</i>\u0000 assistance across a continuum of activities/environments by altering energetic properties of the human body. We previously introduced a port-controlled Hamiltonian framework that parameterizes the control law through basis functions related to gravitational and gyroscopic terms, which are optimized to fit normalized able-bodied joint torques across multiple walking gaits on different ground inclines. However, this approach did not have the flexibility to reproduce joint torques for a broader set of activities, including stair climbing and stand-to-sit, due to strict assumptions related to input-output passivity, which ensures the human remains in control of energy growth in the closed-loop dynamics. To provide biomimetic assistance across all primary activities of daily life, this paper generalizes this energy shaping framework by incorporating vertical ground reaction forces and global planar orientation into the basis set, while preserving passivity between the human joint torques and human joint velocities. We present an experimental implementation on a powered knee-ankle exoskeleton used by three able-bodied human subjects during walking on various inclines, ramp ascent/descent, and stand-to-sit, demonstrating the versatility of this control approach and its effect on muscular effort.","PeriodicalId":73299,"journal":{"name":"IEEE open journal of control systems","volume":"1 ","pages":"15-28"},"PeriodicalIF":0.0,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552933/9683993/09756252.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50237549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Non-Stationary Representation Learning in Sequential Linear Bandits 序列线性带中的非平稳表示学习

IEEE open journal of control systems Pub Date : 2022-03-27 DOI: 10.1109/OJCSYS.2022.3178540

Yuzhen Qin;Tommaso Menara;Samet Oymak;ShiNung Ching;Fabio Pasqualetti

引用次数: 12