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Comprehensive Production Index Prediction Using Dual-Scale Deep learning in Mineral Processing

ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2024年第4期PP卷 6780-6791页

作者： Zhang, Kesheng Yu, Wen Jia, Yao Chai, Tianyou Northeastern Univ Key Lab Integrated Automat Proc Ind Shenyang 110004 Peoples R China Natl Polytech Inst CINVESTAV IPN Dept Control Automat Mexico City 07360 Mexico

In mineral processing, the dynamic nature of industrial data poses challenges for decision-makers in accurately assessing current production statuses. To enhance the decision-making process, it is crucial to predict comprehensive production indices (CPIs), which are influenced by both human operators and industrial processes, and demonstrate a strong dual-scale property. To improve the accuracy of CPIs' prediction, we introduce the high-frequency (HF) unit and low-frequency (LF) unit within our proposed dual-scale deep learning (DL) network. This architecture enables the exploration of nonlinear dynamic mapping in dual-scale industrial data. By integrating the Cloud-Edge collaboration mechanism with DL, our training strategy mitigates the dominance of HF data and guides networks to prioritize different frequency information. Through self-tuning training via Cloud-Edge collaboration, the optimal model structure and parameters on the cloud server are adjusted, with the edge model self-updating accordingly. Validated through online industrial experiments, our method significantly enhances CPIs' prediction accuracy compared to the baseline approaches.

关键词： Minerals Materials processing Predictive models data models Training Indexes Feature extraction Cloud-Edge comprehensive production index (CPI) deep learning (DL) dual-scale mineral processing

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data-driven Adaptive Consensus learning From Network Topologies

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2022年第8期33卷 3487-3497页

作者： Chi, Ronghu Hui, Yu Huang, Biao Hou, Zhongsheng Bu, Xuhui Qingdao Univ Sci & Technol Sch Automat & Elect Engn Inst Artificial Intelligence & Control Qingdao 266061 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Henan Peoples R China

The problem of consensus learning from network topologies is studied for strongly connected nonlinear nonaffine multiagent systems (MASs). A linear spatial dynamic relationship (LSDR) is built at first to formulate the dynamic I/O relationship between an agent and all the other agents that are communicated through the networked topology. The LSDR consists of a linear parametric uncertain term and a residual nonlinear uncertain term. Utilizing the LSDR, a data-driven adaptive learning consensus protocol (DDALCP) is proposed to learn from both time dynamics of agent itself and spatial dynamics of the whole MAS. The parametric uncertainty and nonlinear uncertainty are estimated through an estimator and an observer respectively to improve robustness. The proposed DDALCP has a strong learning ability to improve the consensus performance because time dynamics and network topology information are both considered. The proposed consensus learning method is data-driven and has no dependence on the system model. The theoretical results are demonstrated by simulations.

关键词： Network topology Uncertainty Topology Consensus control Nonlinear dynamical systems Observers Vehicle dynamics Consensus learning data-driven nonaffine nonlinear multiagent systems (MASs) spatial data model spatial dynamic linearization

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Local-Global Dynamic Information Fusion Graph learning for Traffic Flow Prediction 13

Local-Global Dynamic Information Fusion Graph Learning for T...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Xu, Yuan Qin, Fan Ke, Wei He, Yan-Lin Zhang, Ming-Qing Zhu, Qun-Xiong Zhang, Yang College of Information Science & Technology Beijing University of Chemical Technology Beijing100029 China Engineering Research Center of Intelligent Pse Ministry of Education of China Beijing100029 China Macao Polytechnic University Faculty of Applied Sciences 999078 China

ISBN: (纸本)9798350361674

In the field of intelligent transportation systems, accurately predicting traffic flow is a challenging endeavor for enabling efficient services in urban road networks. While current research has made significant advancements in capturing nonlinear features and spatiotemporal dependencies at a fixed geographic node level, it often overlooks the dynamic interactions of traffic flow across different regions. In this work, we present a novel local-global dynamic information fusion graph learning model (LGDF-GL) for traffic flow prediction. Different from other graph learning model, we first design a dynamic adjacency matrix by region-adaptive parameter learning, and fuse it with a static adjacency matrix based on node geographic information by dynamic information fusion module, and the fused result serves as the diagonal elements of the local adjacency matrix. Subsequently, we input it into the graph convolutional network (GCN) and gated recurrent unit (GRU) aggregation model to extract local feature information containing dynamic spatiotemporal features from traffic flow data. The local feature information is pruned and concatenated to obtain global feature representations. Then we achieve global feature representation prediction through multiple fully connected layers. Finally, we conducted experiments on two real-world traffic flow datasets to evaluate the performance of our proposed method. The results clearly demonstrate that the LGDF-GL method achieves superior prediction ability compared to several baseline models. © 2024 ieee.

关键词： Information fusion

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Robust Self-learning Fault-Tolerant control for Hypersonic Flight Vehicle Based on ADHDP

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ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第9期53卷 5295-5306页

作者： Liang, Shuai Xu, Bin Zhang, Youmin Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Concordia Univ Dept Mech & Ind Engn Montreal PQ H3G 1M8 Canada

In this article, a robust self-learning fault-tolerant control (FTC) strategy is proposed to deal with the tracking control problem of the hypersonic flight vehicle (HFV) with uncertainties, actuator faults, and external disturbances. First, an adaptive baseline controller is constructed to achieve stable tracking, in which neural networks are introduced to approximate the unknown dynamics, adaptive laws are formulated to compensate the unknown lumped disturbances, and the Nussbaum technique is applied to address the time-varying actuator faults. Then, to improve the command tracking performance of the baseline controller, a data-driven auxiliary controller which can adaptively adjust the action-critic network weights over time along with the tracking deviation to obtain the optimal control signals in the sense of performance index is developed based on action-dependent heuristic dynamic programming technology. Finally, a comprehensive robust self-learning FTC law is constructed by synthesizing the baseline controller and the auxiliary controller, which leads to good robustness and tracking performance of the closed-loop HFV system. The stability and the superiority of the proposed control algorithm are verified by the Lyapunov theory and comparative numerical simulations, respectively.

关键词： Action-dependent heuristic dynamic programming (ADHDP) adaptive control fault-tolerant control (FTC) hypersonic flight vehicle (HFV) neural network (NN)

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Observer-Informed Deep learning for Traffic State Estimation With Boundary Sensing

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2024年第2期25卷 1602-1611页

作者： Zhao, Chenguang Yu, Huan Hong Kong Univ Sci & Technol Guangzhou Thrust Intelligent Transportat Guangzhou 511400 Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China

Traffic state estimation (TSE) refers to the inference of macroscopic traffic states, including density, speed, and flow, based on partially observed traffic data and some prior knowledge of traffic dynamics. TSE plays a key role in traffic management since traffic control relies on accurate estimation of traffic states. This paper proposes a novel hybrid TSE approach called Observer-Informed Deep learning (OIDL), which integrates a Partial Differential Equation (PDE) observer and deep learning paradigm to estimate spatial-temporal traffic states from boundary sensing data. The proposed OIDL consists of two modules, an Observer-Uninformed Neural Network (OUNN) to generate preliminary traffic state estimation, and an Observer-Informed Neural Network (OINN) constructed from a boundary observer with theoretical convergence guarantee to regularize the estimation. Furthermore, we propose Adaptive OIDL (aOIDL) to simultaneously estimate traffic states and model parameters. Experiments on the NGSIM dataset demonstrate that the proposed OIDL reduces the estimation error by up to 30 percent compared to the model-based observer, data-driven neural networks, and some hybrid TSE approaches. The OIDL also has smaller variance of the estimation error and presents more accurate pattern for congested traffic.

关键词： Traffic state estimation boundary observer adaptive observer observer-informed deep learning

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Distributed control of Heterogeneous Modular Flight Arrays under the Limited Space Constraints 13

Distributed Control of Heterogeneous Modular Flight Arrays u...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Liu, Jianhui Yang, Chunxi Zhang, Xiufeng Li, Yiming Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan650500 China

ISBN: (纸本)9798350361674

Modularized aerial vehicles are now receiving more and more attention for its flexibility and scalability. How to achieve fast and safe traversal among modular unmanned aerial vehicle (UAV) swarms in the limited space is a critical issue for them to accomplish complex missions. Motivated by cooperative control method of UAV swarms in structured airspace, this paper takes the modular flight array platform developed by our research team as an object. And then an advection model is established to discuss the dynamic process of multiple modular flight arrays. By designing two regions, a type of collision avoidance schedule is proposed. Moreover, a distributed controller based on the group of heterogeneous modular flight arrays is developed to solve air traffic problem among them under limited space constraints. Formal analyses are made to show that all modular flight arrays can reach their respective destination lines and avoid collisions. In order to make the modular flight arrays complete their respective missions quickly, a collision avoidance strategy is proposed, which shorten the convergence time and effectively avoids the oscillation phenomenon. Finally, simulation example is given to verify the effectiveness of the proposed method. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Model-Guided learning for Wind Farm Power Optimization

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ieee TRANSACTIONS ON control systems TECHNOLOGY 2024年第2期32卷 428-439页

作者： Xu, Zhiwei Chu, Bing Geng, Hua Nian, Xiaohong Zhang, Chenghui Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Cent South Univ Sch Automat Changsha 410075 Peoples R China

In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Accurately modeling the interactions is challenging due to the highly complex nature of the wakes and this limits the performance of model-based wind farm power optimization methods. There are also data-driven approaches, which do not require a system model. However, they generally require a large number of measurement data and the convergence speed can be slow. To address these limitations, this article proposes a model-guided learning (MGL) method for wind farm to improve its power output by leveraging the knowledge of the available simplified power generation model and learning from the real-time power generation data. The proposed method can quickly increase the power output of the wind farm, guarantee implemented control actions to satisfy the control constraints of all turbines, and have the ability to find the optimal solution of the power optimization problem. The presented method is then extended to deal with time-varying wind conditions using a hierarchical framework. Simulation results indicate that the proposed scheme can efficiently improve the power output of the wind farm in different wind conditions compared with some benchmarks. It shows a power efficiency gain of 2.5% over greedy policy and 1.2% than the model-based gradient method in given complex wind conditions, which are substantial improvements in the performance for the considered wind farm power optimization problem.

关键词： Cooperative control model uncertainties power optimization wake interactions wind farm

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Reinforcement learning Environment for Cyber-Resilient Power Distribution System

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ieee ACCESS 2023年 11卷 127216-127228页

作者： Sahu, Abhijeet Venkatraman, Venkatesh Macwan, Richard Natl Renewable Energy Lab Boulder CO 80305 USA

Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. Reinforcement learning (RL)-based techniques provide a credible alternative to conventional, optimization-based solvers especially when there is uncertainty in the environment, such as renewable generation or cyber system performance. Efficiently training an agent, however, requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for power systems, and, similarly, there are environments for communication systems. Most cyber system simulators are based in a UNIX environment, while the power system simulators are based in the Windows operating system. Hence the generation of a cyber-physical, mixed-domain RL environment has been challenging. Existing co-simulation methods are efficient, but are resource and time intensive to generate large-scale data sets for training RL agents. Hence, this work focuses on the development and validation of a mixed-domain RL environment using OpenDSS for the power system and leveraging a discrete event simulator Python package, SimPy for the cyber system, which is operating system agnostic. Further, we present the results of co-simulation and training RL agents for a cyber-physical network reconfiguration and Volt-Var control problem in a power distribution feeder.

关键词： OpenDSS SimPy OpenAI gym network reconfiguration re-routing Reinforcement learning

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Cybersecurity Analysis of data-driven Power System Stability Assessment

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ieee INTERNET OF THINGS JOURNAL 2023年第17期10卷 15723-15735页

作者： Zhang, Zhenyong Zuo, Ke Deng, Ruilong Teng, Fei Sun, Mingyang Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China Guizhou Univ Coll Comp Sci & Technol Guiyang 550025 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

Machine learning-based intelligent systems enhanced with Internet of Things (IoT) technologies have been widely developed and exploited to enable the real-time stability assessment of a large-scale electricity grid. However, it has been extensively recognized that the IoT-enabled communication network of power systems is vulnerable to cyberattacks. In particular, system operating states, critical attributes that act as input to the data-driven stability assessment, can be manipulated by malicious actors to mislead the system operator into making disastrous decisions and thus cause major blackouts and cascading events. In this article, we explore the vulnerability of the data-driven power system stability assessment, with a special emphasis on decision tree-based stability assessment (DTSA) approaches, and investigate the feasibility of constructing a physics-constrained adversarial attack (PCAA) to undermine the DTSA. The PCAA is formulated as a nonlinear programming problem considering the misclassification constraint, power limits, and bad data detection, computing potential adversarial perturbations that reverse the "stable/unstable" prediction of the real-time input while remaining invisible/stealthy. Extensive experiments based on the ieee 68-bus system are conducted to evaluate the impact of PCAAs on predictions of DTSA and their transferability.

关键词： Adversarial examples cyber security decision trees (DTs) physics-constrained machine learning (ML) transient stability assessment (TSA)

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Receding-Constraint Model Predictive control using a Learned Approximate control-Invariant Set

Receding-Constraint Model Predictive Control using a Learned...

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ieee International conference on Robotics and Automation (ICRA)

作者： Lunardi, Gianni La Rocca, Asia Saveriano, Matteo Del Prete, Andrea Univ Trento Ind Engn Dept Via Sommarive 11 I-38123 Trento Italy

ISBN: (纸本)9798350384581;9798350384574

In recent years, advanced model-based and data-driven control methods are unlocking the potential of complex robotics systems, and we can expect this trend to continue at an exponential rate in the near future. However, ensuring safety with these advanced control methods remains a challenge. A well-known tool to make controllers (either Model Predictive controllers or Reinforcement learning policies) safe, is the so-called control-invariant set (a.k.a. safe set). Unfortunately, for nonlinear systems, such a set cannot be exactly computed in general. Numerical algorithms exist for computing approximate control-invariant sets, but classic theoretic control methods break down if the set is not exact. This paper presents our recent efforts to address this issue. We present a novel Model Predictive control scheme that can guarantee recursive feasibility and/or safety under weaker assumptions than classic methods. In particular, recursive feasibility is guaranteed by making the safe-set constraint move backward over the horizon, and assuming that such set satisfies a condition that is weaker than control invariance. Safety is instead guaranteed under an even weaker assumption on the safe set, triggering a safe task-abortion strategy whenever a risk of constraint violation is detected. We evaluated our approach on a simulated robot manipulator, empirically demonstrating that it leads to less constraint violations than state-of-the-art approaches, while retaining reasonable performance in terms of tracking cost, number of completed tasks, and computation time.

关键词： Predictive control systems

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