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Data-Driven Distributionally Robust Mixed-Integer control through Lifted control Policy

IEEE Transactions on Automatic control 2025年

作者： Ma, Xutao Ning, Chao Du, Wenli Shi, Yang Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 3P6 Canada

This paper investigates the finite-horizon distributionally robust mixed-integer control (DRMIC) of uncertain linear systems. However, deriving an optimal causal feedback control policy to this DRMIC problem is computationally formidable for most ambiguity sets. To address the computational challenge, we propose a novel distributionally robust lifted control policy (DR-LCP) method to derive a high-quality approximate solution to this DRMIC problem for a rich class of Wasserstein metric-based ambiguity sets, including the Wasserstein ambiguity set and its variants. In theory, we analyze the asymptotic performance and establish a tight non-asymptotic bound of the proposed method. In numerical experiments, the proposed DR-LCP method empirically demonstrates superior performance compared with existing methods in the literature. ©1963-2012 IEEE.

关键词： Mixed-integer linear programming

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Sparse Gaussian Feature Learning for Nonlinear Industrial Fault Diagnosis

Sparse Gaussian Feature Learning for Nonlinear Industrial Fa...

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第34届中国控制与决策会议

作者： Jianfeng Zhang Chunhui Zhao Jinliang Ding Youxian Sun Xianling Li State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Science and Technology on Thermal Energy and Power Laboratory Wuhan 2nd Ship Design and Research Institute

ISBN: (纸本)9781665478977

With the increasing complexity of modern industrial processes,the high-dimensional industrial data exhibits strong nonlinearity,bringing considerable challenges to the fault diagnosis of *** efficiently extract deep meaningful features that are crucial for fault diagnosis,a sparse Gaussian feature extractor(SGFE) is designed to learn a nonlinear mapping that projects the raw data into the feature space with the fault label *** feature space is described by the one-hot encoding of the fault category label as an orthogonal *** this way,the sparse Gaussian features related to fault categories can be gradually learned from the raw data by *** the feature space,the sparse Gaussian(SG) loss function is designed to constrain the distribution of features to multiple sparse multivariate Gaussian *** sparse Gaussian features are linearly separable in the feature space,which can improve the accuracy of the downstream fault classification *** effectiveness of the proposed SGFE is verified by the Tennessee-Eastman(TE) benchmark process.

关键词： Nonlinear fault diagnosis sparse Gaussian feature learning Gaussian feature extractor

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Input-output finite-time sliding mode fault-tolerant control for Markovian jump systems with incomplete transition rates

Input-output finite-time sliding mode fault-tolerant control...

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Chinese control and Decision Conference, CCDC

作者： Meng Zhao Haijuan Zhao Zhiru Cao Yugang Niu Ministry of Education The Key Laboratory of Smart Manufacturing in Energy Chemical Process (East China University of Science and Technology) Shanghai China School of Information and Control Engineering Qingdao University of Technology Qingdao China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9781665478977

This paper deals with the input-output finite-time sliding mode control (SMC) problem of a class of uncertain Markovian jump systems with incomplete transition rates. It is assumed that the actuator failure may occur. A mode-dependent sliding surface is constructed. By synthesizing a fault-tolerant SMC scheme, the specified sliding surface can be reached in finite time. Furthermore, the input-output finite-time stability over both the reaching and sliding motion stages is ensured simultaneously by establishing some sufficient conditions. Finally, the developed fault-tolerant SMC approach is verified by a numerical example.

关键词： Fault tolerance Actuators Sufficient conditions Fault tolerant systems Resists Sliding mode control Numerical stability

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Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries

Exponential Weighted Moving Average-Based Variational Mode D...

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Data Driven control and Learning Systems (DDCLS)

作者： Penghao Wu Hongfeng Tao Engang Tian Tangwen Yin Hongtian Chen Yiyang Chen School of Mechanical and Electrical Engineering Soochow University Suzhou P. R. China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi P. R. China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods.

关键词： Fault diagnosis Learning systems Employee welfare Sensitivity Power supplies Noise Electric vehicles

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Existence and Completeness of Bounded Disturbance Observers: A Set-Membership Viewpoint

arXiv

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arXiv 2023年

作者： Li, Yudong Cong, Yirui Dong, Jiuxiang The State Key Laboratory of Synthetical Automation of Process Industries Northeastern University China The College of Information Science and Engineering Northeastern University China The Key Laboratory of Vibration and Control of Aero-Propulsion Systems Ministry of Education of China Northeastern University China The College of Intelligence Science and Technology National University of Defense Technology China

This paper investigates the boundedness of the Disturbance Observer (DO) for linear discrete-time systems. In contrast to previous studies that focus on analyzing and/or designing observer gains, our analysis and synthesis approach is based on a set-membership viewpoint. From this viewpoint, a necessary and sufficient existence condition of bounded DOs is first established, which can be easily verified. Furthermore, a set-membership filter-based DO is developed, and its completeness is proved;thus, our proposed DO is bounded if and only if bounded DOs exist. We also prove that the proposed DO has the capability to achieve the worst-case optimality, which can provide a benchmark for the design of DOs. Finally, numerical simulations are performed to corroborate the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital control systems

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Interpretable Soft Sensors using Extremely Randomized Trees and SHAP

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IFAC-PapersOnLine 2023年第2期56卷 8000-8005页

作者： Liang Cao Xiaolu Ji Yankai Cao Yupeng Li Lim C. Siang Jin Li Vijay Kumar Pediredla R. Bhushan Gopaluni Department of Chemical and Biological Engineering University of British Columbia Vancouver BC V6T 1Z3 Canada Department of Statistics University of British Columbia Vancouver BC V6T 1Z4 Canada School of Automation China University of Geosciences Wuhan 430074 China Department of Process Control Engineering Burnaby Refinery Burnaby BC V5C 1L7 Canada

Tree-based ensemble models are easy to implement and have been widely used in various fields. However, they have limitations in industrial process applications since the majority of tree-based ensemble models are prone to over-fitting. In addition, the internal structure of tree-based ensemble models is very complex and the output of the model is also difficult to explain, which makes its application in industrial soft sensors very challenging. The purpose of this work is to build accurate and interpretable soft sensors for industrial processes. First, to deal with overfitting, a robust tree-based ensemble model and extremely randomized trees are used to build accurate soft sensors. Then, to improve model interpretability, an interpretable machine learning algorithm, namely Shapely additive explanation, is used to infer the global and local contributions of each feature to the predictions. Finally, the effectiveness of the proposed algorithms is validated on real industrial fluid catalytic cracker unit data.

关键词： Interpretability SHAP Extremely Randomized Trees Soft Sensor

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Lexicographic Multi-objective Order Picking Optimization for Robotic Mobile Fulfillment Systems

Lexicographic Multi-objective Order Picking Optimization for...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Hanying Wang Ziyan Zhao Jiaqi Liang Xingyang Li Shixin Liu College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China Département de mathématiques et génie industriel Polytechnique Montréal Montréal Qc Canada Faculty of Robot Science and Engineering Northeastern University Shenyang China State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao Hebei China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In light of advancements in artificial intelligence, the Internet of Things, and mechatronics, robots are increasingly integrated into e-commerce warehouses to enable smart order picking solutions and foster intelligent automation. A robot-assisted order picking process revolutionizes the traditional labor-intensive person-to-goods order picking technology, leading to a goods-to-person (G2P) smart warehouse. Within it, robots transport pods to predefined picking stations, where human pickers retrieve the requested goods from these pods to fulfill customer orders. The allocation of pods to robots and the scheduling of picking operations are key optimization issues in G2P order picking systems. Although they play a key role in improving operational efficiency, existing research has paid limited attention to their joint optimization. This study considers a lexicographic multi-objective optimization problem to shorten the order picking cycles under the premise of optimizing the picking efficiency evaluated by makespan. We build a mixed integer program for the newly proposed problem and develop a matheuristic algorithm by integrating a commodity-order model into a metaheuristic algorithm to solve it. Experimental results show that the proposed method can significantly shorten the total order picking cycles while keeping the minimum makespan. It outperforms a recent state-of-the-art algorithm. This work emphasizes the importance of joint optimization within G2P smart warehouses and reveals the high potential of the proposed method to be used in practice.

关键词： Intelligent automation Adaptation models Mechatronics Metaheuristics Search problems Scheduling Resource management Internet of Things Electronic commerce Robots

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Quantized Distributed Nonconvex Optimization with Linear Convergence

Quantized Distributed Nonconvex Optimization with Linear Con...

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IEEE Conference on Decision and control

作者： Lei Xu Xinlei Yi Jiayue Sun Yang Shi Karl Henrik Johansson Tao Yang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers distributed nonconvex op-timization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels often have limited bandwidth or capacity, we first introduce a quantization rule and an encoder/decoder scheme to reduce the transmission bits. By integrating them with a distributed algorithm, we then propose a distributed quantized nonconvex optimization algorithm. Assuming the global cost function satisfies the Polyak– Łojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that the proposed algorithm linearly converges to a global optimal point. Moreover, a low data rate is shown to be sufficient to ensure linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical simulation examples.

关键词： Quantization (signal) Communication channels Bandwidth Cost function Numerical simulation Communication networks Distributed algorithms

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Supervised Dynamic Latent Variable Models for Fault Identification in Dynamic processes

Supervised Dynamic Latent Variable Models for Fault Identifi...

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Fault Detection, Supervision and Safety for Technical processes (SAFEprocess), CAA Symposium on

作者： Qiqi Niu Chengkai Shen Yuting Lyu Yi Liu Le Zhou School of Automation and Electrical Engineering Zhejiang University of Science & Technology Hangzhou Zhejiang China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou Zhejiang China

ISBN: (纸本)9781665401166

With an increasing application of the multivariate statistical process monitoring method, the fault identification becomes more and more crucial after the fault detection, which is benefited to fault troubleshooting effectively and quickly. To deal with the dynamic process and the impact of random noise on fault identification, a novel fault identification method based on supervised autoregressive dynamic latent variable is proposed in this paper. After the fault label structure is added on the unsupervised model, the relationship between fault information and dynamic latent feature is strengthened. Then, the fault cause is determined based on the fault relevance method. Finally, the validity of method is verified by a numerical example and an actual wastewater treatment case.

关键词： Fault diagnosis process monitoring Bridges Correlation Fault detection Numerical models Safety

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