IEEE Robotics Autom. Mag.最新文献_第5页

Asymptotic Optimality of Myopic Ranking and Selection Procedures 近视眼排序与选择程序的渐近最优性

IEEE Robotics Autom. Mag. Pub Date : 2022-11-27 DOI: 10.48550/arXiv.2211.14723

Yanwen Li, Siyang Gao, Zhongshun Shi

{"title":"Asymptotic Optimality of Myopic Ranking and Selection Procedures","authors":"Yanwen Li, Siyang Gao, Zhongshun Shi","doi":"10.48550/arXiv.2211.14723","DOIUrl":"https://doi.org/10.48550/arXiv.2211.14723","url":null,"abstract":"Ranking and selection (R&S) is a popular model for studying discrete-event dynamic systems. It aims to select the best design (the design with the largest mean performance) from a finite set, where the mean of each design is unknown and has to be learned by samples. Great research efforts have been devoted to this problem in the literature for developing procedures with superior empirical performance and showing their optimality. In these efforts, myopic procedures were popular. They select the best design using a 'naive' mechanism of iteratively and myopically improving an approximation of the objective measure. Although they are based on simple heuristics and lack theoretical support, they turned out highly effective, and often achieved competitive empirical performance compared to procedures that were proposed later and shown to be asymptotically optimal. In this paper, we theoretically analyze these myopic procedures and prove that they also satisfy the optimality conditions of R&S, just like some other popular R&S methods. It explains the good performance of myopic procedures in various numerical tests, and provides good insight into the structure and theoretical development of efficient R&S procedures.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74696761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Convergence Rate Analysis for Optimal Computing Budget Allocation Algorithms 最优计算预算分配算法的收敛率分析

IEEE Robotics Autom. Mag. Pub Date : 2022-11-27 DOI: 10.48550/arXiv.2211.14722

Yanwen Li, Siyang Gao

{"title":"Convergence Rate Analysis for Optimal Computing Budget Allocation Algorithms","authors":"Yanwen Li, Siyang Gao","doi":"10.48550/arXiv.2211.14722","DOIUrl":"https://doi.org/10.48550/arXiv.2211.14722","url":null,"abstract":"Ordinal optimization (OO) is a widely-studied technique for optimizing discrete-event dynamic systems (DEDS). It evaluates the performance of the system designs in a finite set by sampling and aims to correctly make ordinal comparison of the designs. A well-known method in OO is the optimal computing budget allocation (OCBA). It builds the optimality conditions for the number of samples allocated to each design, and the sample allocation that satisfies the optimality conditions is shown to asymptotically maximize the probability of correct selection for the best design. In this paper, we investigate two popular OCBA algorithms. With known variances for samples of each design, we characterize their convergence rates with respect to different performance measures. We first demonstrate that the two OCBA algorithms achieve the optimal convergence rate under measures of probability of correct selection and expected opportunity cost. It fills the void of convergence analysis for OCBA algorithms. Next, we extend our analysis to the measure of cumulative regret, a main measure studied in the field of machine learning. We show that with minor modification, the two OCBA algorithms can reach the optimal convergence rate under cumulative regret. It indicates the potential of broader use of algorithms designed based on the OCBA optimality conditions.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78646730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Hidden Factor estimation in Dynamic Generalized Factor Analysis Models 动态广义因子分析模型中的隐因子估计

IEEE Robotics Autom. Mag. Pub Date : 2022-11-23 DOI: 10.48550/arXiv.2211.12789

G. Picci, Lucia Falconi, A. Ferrante, M. Zorzi

引用次数: 1

Deep Subspace Encoders for Nonlinear System Identification 非线性系统辨识的深子空间编码器

IEEE Robotics Autom. Mag. Pub Date : 2022-10-26 DOI: 10.48550/arXiv.2210.14816

G. Beintema, M. Schoukens, R. T'oth

{"title":"Deep Subspace Encoders for Nonlinear System Identification","authors":"G. Beintema, M. Schoukens, R. T'oth","doi":"10.48550/arXiv.2210.14816","DOIUrl":"https://doi.org/10.48550/arXiv.2210.14816","url":null,"abstract":"Using Artificial Neural Networks (ANN) for nonlinear system identification has proven to be a promising approach, but despite of all recent research efforts, many practical and theoretical problems still remain open. Specifically, noise handling and models, issues of consistency and reliable estimation under minimisation of the prediction error are the most severe problems. The latter comes with numerous practical challenges such as explosion of the computational cost in terms of the number of data samples and the occurrence of instabilities during optimization. In this paper, we aim to overcome these issues by proposing a method which uses a truncated prediction loss and a subspace encoder for state estimation. The truncated prediction loss is computed by selecting multiple truncated subsections from the time series and computing the average prediction loss. To obtain a computationally efficient estimation method that minimizes the truncated prediction loss, a subspace encoder represented by an artificial neural network is introduced. This encoder aims to approximate the state reconstructability map of the estimated model to provide an initial state for each truncated subsection given past inputs and outputs. By theoretical analysis, we show that, under mild conditions, the proposed method is locally consistent, increases optimization stability, and achieves increased data efficiency by allowing for overlap between the subsections. Lastly, we provide practical insights and user guidelines employing a numerical example and state-of-the-art benchmark results.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90843443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Control Design under Actuator Saturation and Multi-Rate Sampling 执行器饱和和多速率采样的控制设计

IEEE Robotics Autom. Mag. Pub Date : 2022-10-19 DOI: 10.48550/arXiv.2210.10868

Francesco Ferrante, R. Sanfelice, S. Tarbouriech

{"title":"Control Design under Actuator Saturation and Multi-Rate Sampling","authors":"Francesco Ferrante, R. Sanfelice, S. Tarbouriech","doi":"10.48550/arXiv.2210.10868","DOIUrl":"https://doi.org/10.48550/arXiv.2210.10868","url":null,"abstract":"The problem of designing a stabilizing feedback controller in the presence of saturating actuators and multi-rate (asynchronous) aperiodic state measurements is studied. Specifically, we consider a scenario in which measurements of the plant states are collected at the controller end in a sporadic and asynchronous fashion. A hybrid controller is used to perform a fusion of measurements sampled at different times. In between sampling events, the controller behaves as a copy of the plant and provides a feedback control signal based on the reconstruction of the plant state. The presence of saturation at the plant input limits the value of the components of this signal to a bounded range. When a new measurement is available, the controller state undergoes an instantaneous jump. The resulting system is augmented with a set of timers triggering the arrival of new measurements and analyzed in a hybrid systems framework. Relying on Lyapunov tools for hybrid systems and techniques for control design under saturation, we propose sufficient conditions in the form of matrix inequalities to ensure regional exponential stability of a closed-set containing the origin of the plant, i.e., exponential stability with a guaranteed region of attraction. Specifically, explicit estimates of the basin of attraction are provided in the form of ellipsoidal sets. Leveraging those conditions, a design procedure based on semidefinite programming is proposed to design a stabilizing controller with maximized size of the basin attraction. The effectiveness of the proposed methodology is shown in an example.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77849061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Stability analysis of optimal control problems with time-dependent costs 具有时变代价的最优控制问题的稳定性分析

IEEE Robotics Autom. Mag. Pub Date : 2022-10-13 DOI: 10.48550/arXiv.2210.07142

Sifeddine Benahmed, R. Postoyan, Mathieu Granzotto, L. Buşoniu, J. Daafouz, D. Nešić

引用次数: 0

Equilibrium pairs trading under delayed cointegration 延迟协整下的均衡对交易

IEEE Robotics Autom. Mag. Pub Date : 2022-10-01 DOI: 10.2139/ssrn.4117238

Tingjin Yan, H. Y. Wong

引用次数: 2

An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications 时空高斯过程回归的有效实现及其应用

IEEE Robotics Autom. Mag. Pub Date : 2022-09-26 DOI: 10.48550/arXiv.2209.12565

Junpeng Zhang, Yue Ju, Biqiang Mu, Renxin Zhong, Tianshi Chen

{"title":"An Efficient Implementation for Spatial-Temporal Gaussian Process Regression and Its Applications","authors":"Junpeng Zhang, Yue Ju, Biqiang Mu, Renxin Zhong, Tianshi Chen","doi":"10.48550/arXiv.2209.12565","DOIUrl":"https://doi.org/10.48550/arXiv.2209.12565","url":null,"abstract":"Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling. Its state-of-art implementation is based on the state-space model realization of the spatial-temporal Gaussian process and its corresponding Kalman filter and smoother, and has computational complexity $mathcal{O}(NM^3)$, where $N$ and $M$ are the number of time instants and spatial input locations, respectively, and thus can only be applied to data with large $N$ but relatively small $M$. In this paper, our primary goal is to show that by exploring the Kronecker structure of the state-space model realization of the spatial-temporal Gaussian process, it is possible to further reduce the computational complexity to $mathcal{O}(M^3+NM^2)$ and thus the proposed implementation can be applied to data with large $N$ and moderately large $M$. The proposed implementation is illustrated over applications in weather data prediction and spatially-distributed system identification. Our secondary goal is to design a kernel for both the Colorado precipitation data and the GHCN temperature data, such that while having more efficient implementation, better prediction performance can also be achieved than the state-of-art result.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76353112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Resilience of Linear Systems to Partial Loss of Control Authority 线性系统对部分失去控制权限的弹性

IEEE Robotics Autom. Mag. Pub Date : 2022-09-16 DOI: 10.48550/arXiv.2209.08034

Jean-Baptiste Bouvier, Melkior Ornik

引用次数: 4

Fixed-Order H-Infinity Controller Design for Port-Hamiltonian Systems port - hamilton系统的定阶h∞控制器设计

IEEE Robotics Autom. Mag. Pub Date : 2022-09-15 DOI: 10.48550/arXiv.2209.07558

P. Schwerdtner, M. Voigt

{"title":"Fixed-Order H-Infinity Controller Design for Port-Hamiltonian Systems","authors":"P. Schwerdtner, M. Voigt","doi":"10.48550/arXiv.2209.07558","DOIUrl":"https://doi.org/10.48550/arXiv.2209.07558","url":null,"abstract":"We present a new fixed-order H-infinity controller design method for potentially large-scale port-Hamiltonian (pH) plants. Our method computes controllers that are also pH (and thus passive) such that the resulting closed-loop systems is again passive, which ensures closed-loop stability simply from the structure of the plant and controller matrices. In this way, we can avoid computationally expensive eigenvalue computations that would otherwise be necessary. In combination with a sample-based objective function which allows us to avoid multiple evaluations of the H-infinity norm (which is typically the main computational burden in fixed-order H-infinity controller synthesis), this makes our method well-suited for plants with a high state-space dimension. In our numerical experiments, we show that applying a passivity-enforcing post-processing step after using well-established H-infinity synthesis methods often leads to a deteriorated H-infinity performance. In constrast to that, our method computes pH controllers, that are automatically passive and simultaneously aim to minimize the H-infinity norm of the closed-loop transfer function. Moreover, our experiments show that for large-scale plants, our method is significantly faster than the well-established fixed-order H-infinity controller synthesis methods.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81931482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0