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Convergent Properties of Riccati Equation with Application to Stability Analysis of State Estimation

Mathematical Problems in engineering 2017年第1期2017卷 1-7页

作者： Cai, X. Ding, Y.S. Li, S.Y. Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai201620 China Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

Since the recursive nature of Kalman filtering always results in a growing size of the optimization problem, state estimation is usually realized by use of finite-memory, receding horizon, sliding window, or "frozen" techniques, which causes difficulties on stability analysis. This paper proposes a novel method on selection of an initial covariance matrix and a horizon for the Kalman filter to make sure that a sequence of the closed-loop Kalman filters are stable as time-invariant filters at subsequent time instant. Convergent properties of Riccati Difference Equation (RDE) are first exploited. Based on these properties, sufficient conditions for stability of a sequence of Kalman filters are obtained. Compared with the existent literature, the convergent properties and the stability conditions are less conservative since they provide analytic results and are applicable to more common cases where the RDEs are not monotonic. © 2017 X. Cai et al.

关键词： Kalman filters

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Optimal tracking cooperative control for multi-agent systems with periodic sampling via robust model predictive control approach 36

Optimal tracking cooperative control for multi-agent systems...

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第36届中国控制会议

作者： Bohui Wang Weisheng Chen Jingcheng Wang Bin Zhang Zhengqiang Zhang Hai Lin Bin Ma the School of Aerospace Science and Technology Xidian University the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China the Autonomous Systems and Intelligent Control international Joint Research Center Xian Technological University the Department of Electrical Engineering University of South Carolina the with the School of Engineering Qufu Normal University

ISBN: (纸本)9781538629185

This paper addresses the optimized tracking cooperative control problem for multi-agent systems with periodic sampling and directed communication topology via robust model predictive control *** proposed optimized tracking cooperative control strategy relaxes the assumptions in existing works that the control gain and the local input must be continuous and the states information exchange has no recourse *** the conditions of the optimized consensus and the communication cost being satisfied,the tracking cooperative control law with bounded parameters is developed based on the periodic *** shows that if the sampling condition is satisfied,the multi-agent systems will reach the optimized *** results are provided to verify the proposed approach.

关键词： Index Terms-multi-agent systems tracking cooperative control periodic sampling optimized consensus robust model predictive control

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Distributed Model Predictive control for Nonlinear Networked systems with Asynchronous Communication 36

Distributed Model Predictive Control for Nonlinear Networked...

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第36届中国控制会议

作者： ZHOU Yuanqiang LI Dewei XI Yugeng CEN Lihui XU Yuli GAN Zhongxue Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education School of Information Science and Engineering Central South University ENN Science & Technology Development Co. Ltd.

ISBN: (纸本)9781538629185

This paper concerns the distributed model predictive control（DMPC） problem for nonlinear multirate networked systems subject to asynchronous *** asynchronization of communication between each subsystem always exists in mechanic engineering and chemical *** motivation of this paper is to use the asynchronous information to improve closed-loop performance for a class of networked systems with coupled nonlinear *** handle the asynchronous communication,a methodology of state trajectory estimation for the coupled neighboring agents over each prediction horizon with guaranteed error bounds is *** make effective use of computational resources,a distributed model predictive control algorithm with the dual-model control strategy is *** conditions for ensuring the subsequent feasibility of the proposed algorithm and the stabilization of the closed-loop networked systems are developed ***,the theoretical results are verified by a simulation example for a set of coupled Van der Pol oscillators.

关键词： Networked systems Nonlinear system Nonlinear predictive control Distributed nonlinear control

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Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

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SENSORS 2020年第3期20卷 726-726页

作者： Lopez-Vazquez, Vanesa Lopez-Guede, Jose Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo DS Labs R+D+I unit of Deusto Sistemas S.A. 01015 Vitoria-Gasteiz Spain Department of System Engineering and Automation Control Faculty of Engineering of Vitoria-Gasteiz University of the Basque Country (UPV/EHU) Nieves Cano 12 01006 Vitoria-Gasteiz Spain se.uhe@zepol.mj Institute of Marine Sciences National Research Council of Italy (CNR) 19032 La Spezia Italy ti.rnc.ramsi.ps@iniram.enomis Stazione Zoologica Anton Dohrn (SZN) 80122 Naples Italy ti.mpvinu@illenaf.e (E.F.) se.cisc.mci@izzugaj (J.A.) Stazione Zoologica Anton Dohrn (SZN) 80122 Naples Italy ti.mpvinu@illenaf.e (E.F.) se.cisc.mci@izzugaj (J.A.) Department of Life and Environmental Sciences Polytechnic University of Marche Via Brecce Bianche 60131 Ancona Italy Institute of Marine Research PO Box 1870 5817 Bergen Norway on.ih@nesnhoj.nepse Stazione Zoologica Anton Dohrn (SZN) 80122 Naples Italy ti.mpvinu@illenaf.e (E.F.) se.cisc.mci@izzugaj (J.A.) Instituto de Ciencias del Mar (ICM) of the Consejo Superior de Investigaciones Científicas (CSIC) 08003 Barcelona Spain

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

关键词： cabled observatories artificial intelligence deep learning machine learning deep-sea fauna

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Stability and Performance Analysis of Saturated systems Using an Enhanced Max Quadratic Lyapunov Function

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IFAC-PapersOnLine 2017年第1期50卷 11847-11852页

作者： Li Y. Lin Z. Li N. Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 United States

This paper revisits the problem of estimating the domain of attraction and the nonlinear L2 gain of a saturated system with an algebraic loop. A max quadratic Lyapunov function, the maximum function of a group of quadratic Lyapunov functions, is proposed for use in the stability and performance analysis for a saturated system. The max quadratic Lyapunov function involves a partitioning of the state space. Exploiting the special properties of regions of the state space, we propose in this paper an enhanced max quadratic Lyapunov function, which results from adding a term that characterizes the regions of the state space to the max quadratic Lyapunov function. The matrix associated with the enhanced max quadratic Lyapunov function is not required to be positive definite, and thus a set of less conservative stability and performance conditions are established, from which a larger estimate of the domain of attraction and a tighter estimate of the nonlinear L2 gain can be obtained. Simulation results indicate the effectiveness and superiority of the proposed method. © 2017

关键词： Domain of attraction enhanced max quadratic Lyapunov functions input saturation L2 gain

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Elements realization of software-algorithmic system optimization of technological processes based on adaptive methods

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IOP Conference Series: Materials Science and engineering 2020年第1期791卷

作者： A L Rutskov E V Sidorenko V L Burkovsky Ya P Fedorov Forecasting department of electricity balance power and loss analysis branch office OJSC «IDGC Centre» - «Voronezhenergo» Arzamaskaya 2 Voronezh 394033 Russia Novovoronezh representation of NR Corporation SA ESKM Moskovsky prospect 179 office building 3 Voronezh 394066 Russia Department of electric drive automation and control in an engineering system Vornezh State Technical University Moscow prospect 179 Voronezh 394066 Russia Corporate finance department and corporate management of the Financial University under the Government of the Russian Federation Leningradsky prospect 49 Mocow 125993 Russia

The paper examines a possibility to optimize complex technological processes of energy resources transfer and consumption. Notes the importance of unidentified and weakly formalized factors consideration, which influence quality of the described object classes functioning. Suggests use of fuzzy neural networks and controllers on their basis for implementation of technological processes optimization of energy resources transfer and consumption at the expense of its dynamic accuracy forecast improvement. Presents algorithmic realization, that later on configured in the MATLAB simulated environment. Quantitative and qualitative parameters, which characterize fuzzy neural controller's usage quality, were received. Later, software environment elements are being described, which constructed on the basis of the suggested adaptive approach for complex technological processes optimization. Points out a possibility to conduct comparative study, and also simulation modeling implementation by way of presented solutions.

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Model and Algorithms for Competitiveness Maximization on Complex Networks

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IFAC-PapersOnLine 2017年第1期50卷 9438-9443页

作者： Zhao J. Liu Q. Wang L. Wang X. Chen G. Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Institute of Complexity Science Qingdao University Qingdao China Department of Electronic Engineering City University of Hong Kong Hong Kong Hong Kong

In this paper we study a competition model on complex networks, where two competing agents are fixed to different states while other agents are evolving to update their states through interactions according to a distributed consensus rule. We consider the situation where one competitor has the opportunity to add new links to other evolving agents such that it could improve its influence on the number of its supporters. We focus on the problem of how to add these new links in order to maximize the influence of a competitor against its rival, referred to as competitiveness. We formulate this competition as a competitiveness maximization problem, which tries to maximize the number of supports of a given competitor against its rival. We analyze the properties of this problem on some special graphs and provide optimal solutions for them, respectively. We design a simulated annealing algorithm and three heuristic algorithms to approximately solve this NP-hard constrained optimization problem. © 2017

关键词： Competitive dynamics complex network heuristic algorithm simulated annealing algorithm

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State-of-charge estimation for li-ion batteries based on multi-strategy probabilities fusion

State-of-charge estimation for li-ion batteries based on mul...

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2017 Chinese automation Congress, CAC 2017

作者： Xiong, Fei Yang, Bo Gao, Yizhao Yang, Jun Chen, Cailian Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538635247

The estimation of state-of-charge (SOC) is the fundamental technology to improve battery working status and life. But severe external environment, dynamic nonlinearity of battery model, and measurement errors make SOC estimation challengable. In this paper, a multi-strategy probabilities based fusion method is proposed. It can combine the dynamic tracking capability of Proportional-Integral Observer (PIO) and noise tolerance capability of extend Kalman filter (EKF). The different abnormal issues, such as internal resistance change, capacity attenuation, data saturation and initialization error, may have a serious impact on the estimates. While the proposed method can guarantee high accuracy estimation and robustness under these issues. It tracks the changes of residual sequence dynamically, and the weighting coefficient can be real-time adjusted according to Bayesian analysis. In simulation, we test the algorithm performance under a variety of current load conditions and abnormal issues. Compared to other model-based methods, the proposed approach performs higher accuracy and robustness with moderate computational complexity. © 2017 IEEE.

关键词： Lithium-ion batteries

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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Chinese control Conference

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai China The Center for Bioinformatics and Computational Biology East China Normal University Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and high-dimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment decision. However, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model (SAE-SVM), for breast cancer diagnosis. The stacked auto-encoders with the greedy layer-wise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Breast cancer Feature extraction Machine learning Support vector machines Electronic mail Principal component analysis

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Cascaded PI controller Tuning for Power Plant Superheated Steam Temperature based on Multi-Objective Optimization

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IFAC-PapersOnLine 2017年第1期50卷 3227-3231页

作者： Fu H. Pan L. Xue Y.L. Sun L. Li D.H. Lee K.Y. Wu Z.L. He T. Zheng S. Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education Southeast University Nanjing 210096 China State Key Lab of Power System Department of Thermal Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Baylor University Waco TX 76798 United States Department of Automation Fuzhou University Fuzhou 350000 China

To improve the superheated steam temperature (SST) control performance, this paper considers the conflicting objectives in SST control, multi-objective optimization (MOO) algorithm is employed in this paper. Firstly, the inner-loop PI controller is optimized by considering the objectives of set-point tracking and disturbance rejection, with and without constraints on robustness level. It is found that robustness constraint is essential in the optimization. Secondly, the regulation performance of the outer-loop PI controller is addressed by optimizing the disturbance rejection and robustness simultaneously. The parameter are determined based on the simulation comparison between the non-dominated solutions. The simulation results demonstrate a promising prospect of promoting the proposed strategy to more industrial practices. © 2017

关键词： Disturbance rejection Pareto optimization Power plant control Robustness Superheated steam temperature

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