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Iterative Learning control for Switched Systems in the Presence of Input Saturation ⁎

IFAC-PapersOnLine 2020年第2期53卷 1444-1449页

作者： Pavel Pakshin Julia Emelianova Eric Rogers Krzysztof Galkowski Arzamas Polytechnic Institute of R.E. Alekseev Nizhny Novgorod State Technical University 19 Kalinina Street Arzamas 607227Russia School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK Institute of Control and Computation Engineering Universityof Zielona Góra ul. Szafrana 2 65-516 Zielona Góra Poland

The paper considers iterative learning control for differential and discrete switched linear systems with control input saturation. A new design is developed based on the use of common vector Lyapunov functions. An ex... 详细信息

关键词： iterative learning control switched systems input saturation repetitive processes stability vector Lyapunov function

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DeepContact: High throughput quantification of membrane contact site based on electron microscopy imaging

DeepContact: High throughput quantification of membrane cont...

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2022生物物理大会

作者： Liqing Liu Shuxin Yang Yang Liu Xixia Li Junjie Hu Li Xiao Tao Xu National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Center for Biological Imaging Institute of Biophysics Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences College of Life Science University of Chinese Academy of Sciences Ningbo HuaMei Hospital University of Chinese Academy of Sciences School of Biomedical Engineering Guangzhou Medical University

Membrane contact site (MCS)-mediated organelle interactions plays essential roles in the cell. Quantitative analysis of the MCS reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool is lacking. Here, we developed "DeepContact", a deep learning protocol for optimizing organelle segmentation and contact analysis based on label-free electron microscopy(EM).

关键词： DeepContact High throughput quantification of membrane contact site based on electron microscopy imaging

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From Standard ZND (Zhang Neural Dynamics) Solving Time-Variant Linear System (LS) to Elegant-Formula ZND Solving Time-Invariant LS Linked to Jacobi Iteration

From Standard ZND (Zhang Neural Dynamics) Solving Time-Varia...

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Chinese control and Decision Conference, CCDC

作者： Wuyi Yang Xiao Liu Liyu Qin Yunong Zhang School of Artificial Intelligence Sun Yat-sen University Guangzhou P. R. China Guangdong Key Laboratory of Modern Control Technology Guangzhou P. R. China School of Electronics and Information Technology Sun Yat-sen University Guangzhou P. R. China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P. R. China

ISBN: (纸本)9781665478977

Solving linear system (LS) is a common and important issue in both academic and industrial fields. There are many methods to solve LS, including neural network (specifically, neural dynamics) and traditional numerical computation algorithms. Besides, traditional numerical computation algorithms comprise, but not limited to, Jacobi iteration and Gauss-Seidel iteration algorithms. On the other hand, neural network algorithms have been the hot topics in research for a long time, which are applied in various fields. In addition, standard Zhang neural dynamics (ZND) is a special kind of neural network to solve some time-variant problems effectively. In this paper, an elegant-formula ZND (EFZND) algorithm is obtained through theoretical derivation. Then, the standard ZND and different EFZND models for solving LS are introduced. Furthermore, the possible relationship between neural dynamics and traditional numerical computation algorithms, especially Jacobi iteration algorithm, is discussed from two specific EFZND models for solving LS. Finally, the feasibility and efficiency of standard ZND and EFZND models for solving LS are proved through derivation.

关键词： Jacobian matrices Linear systems Heuristic algorithms Computational modeling Neural networks Numerical models Computational efficiency

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Repetitive process based design of PD-type iterative learning control laws for batch processes with time-delays

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IFAC-PapersOnLine 2023年第2期56卷 1901-1906页

作者： Robert Maniarski Wojciech Paszke Eric Rogers Hongfeng Tao Institute of Automation Electronic and Electrical Engineering University of Zielona Góra ul. Szafrana 2 65-516 Zielona Góra Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi 214122 P. R. China

This paper uses linear matrix inequality techniques and the Kalman-Yakubovich-Popov lemma to design an iterative learning control law for a class of batch processes with state delays. The design procedure is based on the stability theory for repetitive processes, a class of 2D systems. A numerical example illustrates the new design and demonstrates that the design has advantages compared to the existing alternatives.

关键词： Iterative learning control repetitive processes batch processes linear matrix inequalities systems with delays

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Stability and Stabilization of Nonlinear 2D Markovian Jump Systems with Applications

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IFAC Proceedings Volumes 2013年第11期46卷 695-700页

作者： Julia Emelianova Pavel Pakshin Krzysztof Gałkowski Eric Rogers Arzamas Polytechnic Institute of R.E. Alekseev Nizhny Novgorod State Technical University 19 Kalinina Street Arzamas 607227 Russia Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 65-246 Zielona Góra Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK

This paper develops a vector Lyapunov function based approach to the stability of continuous and discrete 2D nonlinear systems with Markovian jumps. Nonlinear continuous-time 2D systems described by a Roesser model and nonlinear discrete-time repetitive process with Markovian jumps are considered, for which global asymptotic stability in the mean square is defined and sufficient conditions for the existence of this property obtained in terms of stochastic vector Lyapunov functions. These conditions are then applied to iterative learning control design for a set of linear systems with jumps that are controlled over a network. The resulting networked ILC model has the form of repetitive process with a Markovian random structure. The ILC convergence problem is reduced to a stochastic stabilization problem, where the use of a stochastic Lyapunov function as a vector of quadratic forms results in control law design algorithms that can be computed using linear matrix inequalities.

关键词： Nonlinear 2D systems onnlinear repetitive processes Markovian jumps stability iterative learning control

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Self-adaptation of chimera states

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Physical Review E 2019年第1期99卷 010201(R)-010201(R)页

作者： Nan Yao Zi-Gang Huang Hai-Peng Ren Celso Grebogi Ying-Cheng Lai Department of Applied Physics Xi'an University of Technology Xi'an 710048 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs and Institute of Health and Rehabilitation Science School of Life Science and Technology Xi'an Jiaotong University Xi'an 710049 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China Institute for Complex Systems and Mathematical Biology King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of “intelligence” in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

关键词： Chimera states Collective dynamics Coupled oscillators Chaos & nonlinear dynamics

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Adaptive Islanding Detection and Diagnosis Using Wide Area Monitoring

Adaptive Islanding Detection and Diagnosis Using Wide Area M...

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IEEE PES Innovative Smart Grid Technologies Conference Europe

作者： Mark Rafferty Xueqin (Amy) Liu David Laverty Lei Xie Sean McLoone School of Electronics Electrical Engineering and Computer Science Queen's University Belfast State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou PR China

This paper proposes a method for the detection and diagnosis of power system islanding events using phase-angle monitoring. The method is based on multiblock principal component analysis (MB-PCA), an extension of PCA that allows the underlying relationship between data sets to be identified. MB-PCA divides the large-scale system data into several blocks thus enhancing the model's ability to explain and diagnose power system events. Also incorporated into the method is a moving window technique. This allows the method to update over time enhancing power system situational awareness, by using data that best represents the current condition of the time-varying power system. Using Hotelling's T~2 and Q statistics allows the method to detect specific power system events with the main emphasis placed on the detection of power system islands. Further diagnostic approaches, such as contribution plots, are used to locate and isolate the detected events. The reliability of the proposed method is demonstrated using simulated case studies showing the method's ability to detect a genuine islanding event and its avoidance of nuisance tripping.

关键词： Islanding detection and diagnosis Wide Area Monitoring Multiblock principal component analysis Moving window

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A Study on Tuning Methods of Linear Active Disturbance Rejection control for a Class of Thermal Processes

A Study on Tuning Methods of Linear Active Disturbance Rejec...

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International Conference on Information science and control engineering (ICISCE)

作者： Bo Li Li Wang Rui Zhao Shuang Zhang Ming Sun Inner Mongolia Branch Thermal Power Technology Research Institute China Datang Corporation Science and Technology Research Institute Co. Ltd Huhhot China Thermal Power Technology Research Institute China Datang Corporation Science and Technology Research Institute Co. Ltd Beijing China School of Control and Computer Engineering North China Electric Power University Baoding China

Active disturbance rejection controllers have been applied in a certain range in industrial processes. Aiming at the control difficulties of a class of large inertia and large delay thermal processes in thermal power plant, this paper studies two second-order linear ADRC parameter setting methods, and discusses their control effects and applicability. Based on the known process model, one method is to transform model into the form of $K/(Ts+1)^{n}$, and then based on the given maximum sensitivity function, the controller parameter tuning formula is obtained through the asymptote of the Nyquist curve; the other is to transform model into the form of $Ke^{-\tau s}/(Ts+1)$, and then the controller parameters are designed with this approximate model. Taking the superheated steam temperature system of a 330MW thermal power unit as the controlled object, the simulation study was performed by LADRC. The calculation of the control efficiency index and the integration index of ITAE and ISE is carried out from the three aspects, which are set-point tracking, robust performance and noise test. The control effect and scope of application of the two empirical setting formulas are analyzed and the valuable guidance is given.

关键词： Temperature sensors Robust control Sensitivity Process control Transforms Robustness Temperature control

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21.1:Invited Paper:EEG-based comparative measurement of visual fatigue caused by stereoscopic depth perception

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SID Symposium Digest of Technical Papers 2023年第S1期54卷

作者： Lixiu Jia Sheng Huang Yiquan Ding Lixin Jia Lihang Feng Xiaohua Huang School of Computer Engineering Nanjing Institute of Technology Nanjing Jiangsu China School of Electrical Engineering and Control Science Nanjing Tech University Nanjing Jiangsu China

In this study, we investigated the influence of stereoscopic depth on visual fatigue based on subjective and EEG measurement. 19 channels of EEG data of 17 subjects before and after watching 3D movie with different depth ranges were recorded, and the power of δ, θ, α, β, power spectral parameters (gravity frequency, power spectral entropy) were calculated and analyzed. The results showed that visual fatigue caused by 3D enhanced movies with larger negative disparity was more significantly severe than that of 3D normal movies.

关键词： Visual fatigue EEG stereoscopic depth objective measurement

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