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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

Shadow operator: Effective dynamic load change operation training in air separation processes based on industrial nonlinear MPC and Bloom’s taxonomy

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arXiv 2023年

作者： Yang, Guanghui Shao, Zhijiang Wang, Rui Xu, Zuhua Cui, Lidan State Key Lab of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Alibaba–Zhejiang University Joint Research Institute of Frontier Technologies Alibaba Group Hangzhou310027 China Department of Psychology and Behavioral Sciences Zhejiang University Hangzhou310027 China

A novel human–machine interactive training method for dynamic load change operation in air separation processes (ASPs) is proposed. A shadow operator (SO) is developed in this method to train ASP operators through industrial model predictive control (IMPC) and Bloom’s taxonomy. First, a nonlinear two-layer IMPC machine algorithm is developed for dynamic load change operation. The IMPC uses a linear parameter varying prediction model and an iterative multi-step linearization algorithm to compute accurate control decisions. Second, a hierarchical human–machine cooperation model is established to improve the effectiveness of operation training. The model is inspired by an educational psychology framework (Bloom’s taxonomy) and assists ASP operators in enhancing their dynamic operational skills. Finally, five dynamic training modes of the SO are designed based on the IMPC algorithm and the human–machine cooperation model. The practical application results demonstrate that the SO improves the effectiveness of skill acquisition for novice operators and the safety of dynamic operations. Copyright © 2023, The Authors. All rights reserved.

关键词： Model predictive control

Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

Robust Optimization for Quantum Reinforcement Learning control using Partial Observations

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arXiv 2022年

作者： Jiang, Chen Pan, Yu Wu, Zheng-Guang Gao, Qing Dong, Daoyi State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia

The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the number of required quantum measurements on the number of qubits. In this paper, we investigate a robust reinforcement learning method using partial observations to overcome this difficulty. This control scheme is compatible with near-term quantum devices, where the noise is prevalent and predetermining the dynamics of quantum state is practically impossible. We show that this simplified control scheme can achieve similar or even better performance when compared to the conventional methods relying on full observation. We demonstrate the effectiveness of this scheme on examples of quantum state control and quantum approximate optimization algorithm. It has been shown that high-fidelity state control can be achieved even if the noise amplitude is at the same level as the control amplitude. Besides, an acceptable level of optimization accuracy can be achieved for QAOA with noisy control Hamiltonian. This robust control optimization model can be trained to compensate the uncertainties in practical quantum computing. © 2022, CC BY.

关键词： Robust control

GPA-Teleoperation: Gaze Enhanced Perception-aware Safe Assistive Aerial Teleoperation

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arXiv 2021年

作者： Wang, Qianhao He, Botao Xun, Zhiren Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China School of Automation Nanjing Institute of Technology Nanjing211112 China

Gaze is an intuitive and direct way to represent the intentions of an individual. However, when it comes to assistive aerial teleoperation which aims to perform operators' intention, rare attention has been paid to gaze. Existing methods obtain intention directly from the remote controller (RC) input, which is unfriendly to non-professional operators, as the experimental results show in Sec. VI-C. Further, most teleoperation works do not consider environment perception which is vital to guarantee safety. In this paper, we present GPA-Teleoperation, a gaze enhanced perception-aware assistive teleoperation framework, which addresses the above issues systematically. We capture the intention utilizing gaze information, and generate a topological path matching it. Then we refine the path into a safe and feasible trajectory which simultaneously enhances the perception awareness to the environment which the operator is interested in. Additionally, the proposed method is integrated into a customized quadrotor system. Extensive challenging indoor and outdoor real-world experiments and benchmark comparisons verify that the proposed system is reliable, robust and applicable to even unskilled users. We will release the source code of our system to benefit related researches. © 2021, CC BY-NC-ND.

关键词： Remote control

Intention-Aware Planner for Robust and Safe Aerial Tracking

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arXiv 2023年

作者： Ren, Qiuyu Yu, Huan Dai, Jiajun Zheng, Zhi Meng, Jun Xu, Li Xu, Chao Gao, Fei Cao, Yanjun The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou313000 China The Robotics Institute Zhejiang University Yuyao315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to specifically evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Aircraft detection

The Visual-Inertial-Dynamical Multirotor Dataset

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arXiv 2021年

作者： Zhang, Kunyi Yang, Tiankai Ding, Ziming Yang, Sheng Ma, Teng Li, Mingyang Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China Alibaba DAMO Academy Autonomous Driving Lab Hangzhou311121 China

Recently, the community has witnessed numerous datasets built for developing and testing state estimators. However, for some applications such as aerial transportation or search-and-rescue, the contact force or other disturbance must be perceived for robust planning and control, which is beyond the capacity of these datasets. This paper introduces a Visual-Inertial-Dynamical (VID) dataset, not only focusing on traditional six degrees of freedom (6-DOF) pose estimation but also providing dynamical characteristics of the flight platform for external force perception or dynamics-aided estimation. The VID dataset contains hardware synchronized imagery and inertial measurements, with accurate ground truth trajectories for evaluating common visual-inertial estimators. Moreover, the proposed dataset highlights rotor speed and motor current measurements, control inputs, and ground truth 6-axis force data to evaluate external force estimation. To the best of our knowledge, the proposed VID dataset is the first public dataset containing visual-inertial and complete dynamical information in the real world for pose and external force evaluation. The dataset and related files are open-sourced. © 2021, CC BY.

关键词： Degrees of freedom (mechanics)

Self-Enhanced Oxidation of Fluoroquinolone Antibiotics by Cuii/Peroxymonosulfate: Copper Coordination Mechanism with Antibiotics

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SSRN

SSRN 2024年

作者： Zhao, Qing Chen, Long Wang, Zhiqiang Zhou, Lei Gong, Xueqing Xiu, Guangli Shanghai Environmental Protection Key Laboratory for Environmental Standard and Risk Management of Chemical Pollutants School of Resources & Environmental Engineering East China University of Science and Technology Shanghai200237 China State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes School of Resources & Environmental Engineering East China University of Science and Technology Shanghai200237 China State Key Laboratory of Green Chemical Engineering and Industrial Catalysis Centre for Computational Chemistry Research Institute of Industrial Catalysis School of Chemistry and Molecular Engineering East China University of Science and Technology Shanghai200237 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China

Trace copper ions are commonly found in different water bodies. They can effectively promote the degradation of various pollutants by peroxymonosulfate (PMS). However, the underlying mechanism of copper's action has not yet been clarified. Fluoroquinolone antibiotics (FQs), which are commonly used in humans and animals, often coexisting with copper metal in environmental waters, especially in aquaculture and medical wastewater. The presence of CuII under neutral conditions is found to significantly enhance the degradation efficiency of FQs by PMS. The coordination of CuII with FQs plays a crucial role in this process. Taking pefloxacin (PE), one typical FQs as an example, the structure of the copper complexes was determined using UV-visible spectroscopy, fluorescence spectroscopy, and DFT calculations as [Cu (PE) (H2O)4]2+. Comprehensive quenching experiments and electron paramagnetic resonance (EPR) test indicated that high-valence copper was responsible for the accelerated degradation. The combined results, including PMS decay and DFT calculations, indicated that CuII is initially reduced to CuI at a relatively slow rate and then rapidly oxidized to CuIII in the presence of PMS. The results highlight the valence transition of CuII/CuI/CuIII experienced during PMS activation, and the crucial role of contaminant-metal coordination in the valence transformation of copper. © 2024, The Authors. All rights reserved.

关键词： Copper compounds

An Anomaly Detection Approach Based on Integrated LSTM for IoT Big Data

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Security and Communication Networks 2023年第1期2023卷

作者： Li, Chao Fu, Yuhan Zhang, Rui Liang, Hai Wang, Chonghua Li, Junjian Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510700 China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China COMAC Shanghai Aircraft Manufacturing Co. Ltd Beijing100005 China China Industrial Control Systems Cyber Emergency Response Team Beijing100040 China

Due to the expanding scope of Industry 4.0, the Internet of Things has become an important element of the information age. cyber security relies heavily on intrusion detection systems for Internet of Things (IoT) devices. In the face of complex network data and diverse intrusion methods, today's network security environment requires more suitable machine learning methods to meet its security needs, and the current machine learning methods are hardly competent. In part because of network attacks by intruders using cutting-edge techniques and the constrained environment of IoT devices themselves, the most widely used algorithms in recent years include CNN and LSTM, with the former being particularly good at extracting features from the original data space and the latter concentrating more on temporal features of the data. We aim to address the issue of merging spatial and temporal variables in intrusion detection models by introducing a fusion model CNN and C-LSTM in this paper. Fusion features enhanced parallelism in the training process and better results without a very deep network, giving the model a shorter training time, fast convergence, and computational speed for emerging resource-limited network entities. This model is more suitable for anomaly detection tasks in the resource-constrained and time-sensitive big data environment of the Internet of Things. KDDCup-99, a publicly available IBD dataset, was applied in our experiments to demonstrate the model's validity. In comparison to existing deep learning implementations, our proposed multiclass classification model delivers higher accuracy, precision, and recall. © 2023 Chao Li et al.

关键词： Internet of things