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Distributionally Robust Fault Detection by using Kernel Density Estimation ⁎

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IFAC-PapersOnLine 2020年第2期53卷 652-657页

作者： Ting Xue Maiying Zhong Lijia Luo Linlin Li Steven X. Ding Institute for Automatic Control and Complex Systems University of Duisburg-Essen Germany College of Electrical Engineering and Automation Shandong University of Science and Technology China Institute of Process Equipment and Control Engineering Zhejiang University of Technology China School of Automation and Electrical Engineering University of Science and Technology Beijing China

In this paper, a method of distributionally robust fault detection (FD) is proposed for stochastic linear discrete-time systems by using the kernel density estimation (KDE) technique. For this purpose, an H 2 optimization-based fault detection filter is constructed for residual generation. Towards maximizing the fault detection rate (FDR) for a prescribed false alarm rate (FAR), the residual evaluation issue regarding the design of residual evaluation function and threshold is formulated as a distributionally robust optimization problem, wherein the so-called confidence sets are constituted to model the ambiguity of distribution knowledge of residuals in fault-free and faulty cases. A KDE based solution, robust to the estimation errors in probability distribution of residual caused by the finite number of samples, is further developed to address the targeting problem such that the residual evaluation function, threshold as well as the lower bound of FDR can be achieved simultaneously. A case study on a vehicle lateral control system demonstrates the applicability of the proposed FD method.

关键词： Fault detection distributionally robust optimization kernel density estimation

Teaching MPC: Which way to the promised land?

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arXiv 2021年

作者： Faulwasser, Timm Lucia, Sergio Darup, Moritz Schulze Mönnigmann, Martin Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Laboratory of Process Automation Systems TU Dortmund University Dortmund Germany Control and Cyberphysical Systems TU Dortmund University Dortmund Germany Automatic Control and Systems Theory Ruhr University Bochum Bochum Germany

Since the earliest conceptualizations by Lee and Markus, and Propoi in the 1960s, Model Predictive control (MPC) has become a major success story of systems and control with respect to industrial impact and with respect to continued and wide-spread research interest. The field has evolved from conceptually simple linear-quadratic (convex) settings in discrete and continuous time to nonlinear and distributed settings including hybrid, stochastic, and infinite-dimensional systems. Put differently, essentially the entire spectrum of dynamic systems can be considered in the MPC framework with respect to both-system theoretic analysis and tailored numerics. Moreover, recent developments in machine learning also leverage MPC concepts and learning-based and data-driven MPC have become highly active research areas. However, this evident and continued success renders it increasingly complex to live up to industrial expectations while enabling graduate students for state-of-the-art research in teaching MPC. Hence, this position paper attempts to trigger a discussion on teaching MPC. To lay the basis for a fruitful debate, we subsequently investigate the prospect of covering MPC in undergraduate courses;we comment on teaching textbooks;and we discuss the increasing complexity of research-oriented graduate teaching of MPC. Copyright © 2021, The Authors. All rights reserved.

关键词： Model predictive control

Stochastic configuration networks with robust supervised least squares regression

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Neurocomputing 2025年 647卷

作者： Fei Chu Zihang Sun Yu Wang Yong Zhang School of Information and Control Engineering China University of Mining and Technology Xuzhou 221116 China The State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing 100160 China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou 221116 China

Stochastic Configuration Networks (SCNs) are widely used in regression modeling to fit data distributions due to their fast convergence and powerful learning capabilities. However, in practical industrial applications, labeled outliers in the dataset inevitably appear, which can destroy the internal structure and connection of the data, and not only seriously reduce the prediction accuracy of SCNs, but may also increase the risk of overfitting. To cope with this problem, this paper proposes a new algorithm - Robust Supervised Stochastic Configuration Networks (RSSCNs). In this paper, firstly, the loss function is constructed based on the concave function of the error, which extends the applicability of the robust supervised least squares regression. Second, robust supervised least squares regression is introduced into the SCN algorithm for the first time, which enhances the robustness of the model and reduces the risk of model overfitting. Finally, the inequality constraint of the SCN algorithm is reconstructed according to the loss function, which optimizes the parameter selection and accelerates the convergence of the model. In the experimental section, RSSCNs are applied to a function fitting experiment, several benchmark datasets, and an industrial dataset from a dense medium coal preparation process, and the performance is compared with other algorithms. The experimental results show that for these datasets, RSSCNs perform favorably in terms of accuracy and robustness, validating the effectiveness of the algorithm.

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Communication Compression for Distributed Nonconvex Optimization

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arXiv 2022年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers distributed nonconvex optimization with the cost functions being distributed over agents. Noting that information compression is a key tool to reduce the heavy communication load for distributed algorithms as agents iteratively communicate with neighbors, we propose three distributed primal–dual algorithms with compressed communication. The first two algorithms are applicable to a general class of compressors with bounded relative compression error and the third algorithm is suitable for two general classes of compressors with bounded absolute compression error. We show that the proposed distributed algorithms with compressed communication have comparable convergence properties as state-of-the-art algorithms with exact communication. Specifically, we show that they can find first-order stationary points with sublinear convergence rate O(1/T) when each local cost function is smooth, where T is the total number of iterations, and find global optima with linear convergence rate under an additional condition that the global cost function satisfies the Polyak–Lojasiewicz condition. Numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

Dexterous Manipulation through Imitation Learning: A Survey

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arXiv 2025年

作者： An, Shan Meng, Ziyu Tang, Chao Zhou, Yuning Liu, Tengyu Ding, Fangqiang Zhang, Shufang Mu, Yao Song, Ran Zhang, Wei Hou, Zeng-Guang Zhang, Hong School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Control Science and Engineering Shandong University Jinan250061 China State Key Laboratory of General Artificial Intelligence Beijing100086 China Department of Mechanical and Process Engineering ETH Zurich Zurich8092 Switzerland Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Informatics University of Edinburgh EH8 9AB United Kingdom Department of Computer Science University of Hong Kong 999077 Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology 999078 China

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high-dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning (IL), details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Grippers

Compressed Gradient Tracking Algorithms for Distributed Nonconvex Optimization

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arXiv 2023年

作者： Xu, Lei Yi, Xinlei Wen, Guanghui Shi, Yang Johansson, Karl H. Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Systems Science School of Mathematics Southeast University Nanjing210096 China The Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

In this paper, we study the distributed nonconvex optimization problem, aiming to minimize the average value of the local nonconvex cost functions using local information exchange. To reduce the communication overhead, we introduce three general classes of compressors, i.e., compressors with bounded relative compression error, compressors with globally bounded absolute compression error, and compressors with locally bounded absolute compression error. By integrating them, respectively, with the distributed gradient tracking algorithm, we then propose three corresponding compressed distributed nonconvex optimization algorithms. Motivated by the state-of-the-art BEER algorithm proposed in [1], which is an efficient compressed algorithm integrating gradient tracking with biased and contractive compressors, our first proposed algorithm extends this algorithm to accommodate both biased and non-contractive compressors. For each algorithm, we design a novel Lyapunov function to demonstrate its sublinear convergence to a stationary point if the local cost functions are smooth. Furthermore, when the global cost function satisfies the Polyak–Lojasiewicz (P–L) condition, we show that our proposed algorithms linearly converge to a global optimal point. It is worth noting that, for compressors with bounded relative compression error and globally bounded absolute compression error, our proposed algorithms’ parameters do not require prior knowledge of the P–L constant. The theoretical results are illustrated by numerical examples, demonstrating the effectiveness of the proposed algorithms in significantly reducing the communication burden while maintaining the convergence performance. Moreover, simulation results show that the proposed algorithms outperform the state-of-the-art compressed distributed nonconvex optimization algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

Nonsingular field-of-view constrained cooperative guidance with improved time-to-go estimation

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Chinese Journal of Aeronautics 2025年

作者： Xiaofei YANG Yunjie WU Weibo XIA Xiaodong LIU Haibin DUAN School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China Beĳing Aerospace Automatic Control Institute Beĳing 100854 China

This paper proposes a distributed nonsingular cooperative guidance law for multiple flight vehicles with Field-of-View (FOV) constraints. First, a novel time-to-go estimation is developed based on a FOV-constrained Proportional Navigation Guidance (FPNG) law. Then, the FPNG law is augmented with a cooperative guidance term to achieve consensus of time-to-go with predefined-time convergence prior to the impact time. A continuous auxiliary function is introduced in the bias term to avoid the singularity of guidance command. Moreover, the proposed guidance law is extended to the three-dimensional guidance scenarios and the moving target with the help of a predicted interception point. Finally, several numerical simulations are conducted, and the results verify the effectiveness, robustness, and advantages of the proposed cooperative guidance law.

关键词： Flight vehicles Cooperative guidance Field-of-view constraints Time-to-go estimation Predefined-time convergence

Analysis and control of autonomous mobility-on-demand systems

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arXiv 2021年

作者： Zardini, Gioele Lanzetti, Nicolas Pavone, Marco Frazzoli, Emilio Institute for Dynamic Systems and Control Department of Mechanical and Process Engineering ETH Zürich Zürich Switzerland Automatic Control Laboratory Department of Information Technology and ElectricalEngineering ETH Zürich Zürich Switzerland Autonomous Systems Lab Department of Aeronautics and Astronautics Stanford University StanfordCA United States

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research. © 2021, CC BY-NC-ND.

关键词： Autonomous vehicles

Lecture Notes in Control and Information Sciences

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Preface

Lecture Notes in control and Information Sciences 2023年 492卷 v-vi页

作者： Luan, Xiaoli He, Shuping Liu, Fei Key Laboratory of Advanced Process Control for Light Industry [Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi China Key Laboratory of Intelligent Computing and Signal Processing [Ministry of Education School of Electrical Engineering and Automation Anhui University Anhui Hefei China