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arXiv 2022年

作者： Dai, Wei Ning, Chuanfeng Pei, Shiyu Zhu, Song Wang, Xuesong The School of Information and Control Engineering Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110819 China The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China The School of Mathematics China University of Mining and Technology Xuzhou221116 China

As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning. However, the randomness makes SCNs more likely to generate approximate linear correlative nodes that are redundant and low quality, thereby resulting in non-compact network structure. In the light of a fundamental principle in machine learning, that is, a model with fewer parameters holds improved generalization. This paper proposes orthogonal SCN, termed OSCN, to filtrate out the low-quality hidden nodes for network structure reduction by incorporating Gram-Schmidt orthogonalization technology. The universal approximation property of OSCN and an adaptive setting for the key construction parameters have been presented in details. In addition, an incremental updating scheme is developed to dynamically determine the output weights, contributing to improved computational efficiency. Finally, experimental results on two numerical examples and several real-world regression and classification datasets substantiate the effectiveness and feasibility of the proposed approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Design of ILC laws with conditions for stabilizing linear 2D discrete Roesser models

Design of ILC laws with conditions for stabilizing linear 2D...

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IEEE Conference on Decision and control

作者： Robert Maniarski Wojciech Paszke Hongfeng Tao Eric Rogers Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi China School of Electronics and Computer Science University of Southampton Southampton UK

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers the design of iterative learning control schemes by transforming the task into an equivalent problem of designing stabilizing control laws for the Roesser model for linear two-dimensional systems. Then, based on a non-conservative version of stability and stabilization conditions for linear 2D systems, a control law is designed based on linear matrix inequalities. In addition, some control performance indexes are improved by choosing the locations for the relevant transfer function, leading to faster convergence. Also, an additional linear matrix inequality constraint is developed to minimize the gain of designed controllers and facilitate their practical implementation. Furthermore, the problem of ILC control law design for strictly proper system dynamics using anticipative action. Finally, a numerical example demonstrates the effectiveness of the new results, including advantages when compared to the existing alternatives.

关键词： State feedback Uncertainty System dynamics Transfer functions Stability analysis Linear matrix inequalities Performance analysis

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Two Feature Fusion Network Based on Efficient Deep Residual Shrinkage Spatial-Temporal Graph Convolution for Emotion Recognition from Gaits

Two Feature Fusion Network Based on Efficient Deep Residual ...

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Chinese control and Decision Conference, CCDC

作者： Xiaoqing Liu Sen Zhang Wendong Xiao School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Advanced Control of Iron and Steel Process (Ministry of Education) Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan China Beijing Engineering Research Center of Industrial Spectrum Imaging Beijing China

ISBN: (纸本)9781665478977

Emotion Recognition plays a key role in human social activities. Most current methods choose facial information for emotion recognition. But facial information is easily limited by distance in large scenes. Gait information can break this limitation, but the amount of publicly available data is small. This paper proposes two feature fusion network based on efficient deep residual shrinkage spatial-temporal graph convolution to achieve automatic emotion recognition from gaits in large scenes. Firstly chebyshev polynomial approximation graph convolution is combined with efficient deep residual shrinkage network to obtain efficient deep residual shrinkage spatial-temporal graph convolution network to extract deep features. Then sixteen temporal affective features extracted from gaits are used as input of efficient deep residual shrinkage network to extract new affective features. Finally the deep features is spliced with new affective features to divided emotion into happy,angry,sad and neutral. We performed a ten-flod cross-validation experiment on the E-gait real datasets. The highest recognition accuracy is 0.87, with an average of 0.85. Experimental results show that the proposed model is effective in dealing with emotion recognition promblem from gait in fewer sample situation.

关键词： Emotion recognition Convolution Chebyshev approximation Feature extraction

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Fixed-time Leader-Following Formation control of AUVs Without Velocity Measurements

Research Square

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Research Square 2021年

作者： Gao, Zhenyu Zhang, Yi Guo, Ge The School of Control Engineering Northeastern University Qinhuangdao Campus Qinhuangdao066004 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper is concerned with formation control of autonomous underwater vehicles (AUVs), focusing on improving system convergence speed and overcoming velocity measurement limitation. By employing the fixed-time control theory and command filtering technique, a full state feedback formation algorithm is proposed, which makes the follower AUV track the leader in a given time with all signals in the system globally practically stabilized in fixed time. To avoid degraded control performance due to inaccurate velocity measurement, a fixed-time convergent observer is designed to estimate the velocity of AUVs. We then give an observer-based fixed-time control method, with which acceptable formation tracking performance can be achieved in fixed time without velocity measurement. The effectiveness and performance of the proposed method is demonstrated by numerical simulations. © 2021, CC BY.

关键词： Velocity

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Graph Reinforcement Learning Application to Co-operative Decision-Making in Mixed Autonomy Traffic: Framework, Survey, and Challenges

arXiv

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arXiv 2022年

作者： Liu, Qi Li, Xueyuan Li, Zirui Wu, Jingda Du, Guodong Gao, Xin Yang, Fan Yuan, Shihua School of Mechanical Engineering Beijing Institute of Technology Beijing China Chair of Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Germany School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore639798 Singapore Institute of Dynamic System and Control ETH Zurich Zurich Switzerland

Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaboration decision-making for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) has been widely used in solving decision-making problems. However, the existing DRL-based methods have been mainly focused on solving the decision-making of a single CAV. Using the existing DRL-based methods in mixed autonomy traffic cannot accurately represent the mutual effects of vehicles and model dynamic traffic environments. To address these shortcomings, this article proposes a graph reinforcement learning (GRL) approach for multi-agent decision-making of CAVs in mixed autonomy traffic. First, a generic and modular GRL framework is designed. Then, a systematic review of DRL and GRL methods is presented, focusing on the problems addressed in recent research. Moreover, a comparative study on different GRL methods is further proposed based on the designed framework to verify the effectiveness of GRL methods. Results show that the GRL methods can well optimize the performance of multi-agent decision-making for CAVs in mixed autonomy traffic compared to the DRL methods. Finally, challenges and future research directions are summarized. This study can provide a valuable research reference for solving the multi-agent decision-making problems of CAVs in mixed autonomy traffic and can promote the implementation of GRL-based methods into intelligent transportation systems. The source code of our work can be found at https://***/Jacklinkk/Graph_CAVs. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Quantification of C02 emission reductions from energy efficiency actions and solar photovoltaic generation at the Federal Institute of Education, Science, and Technology of Goiás

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2021年 19卷 45-50页

作者： Vidal, M.A. Faria, A.F. Stach, A.H.M. Netou, D.P. Pinto, L.S. Pinlio, A.G. Domingues, E.G. Master’s Program in Technology of Sustainable Process Federal Institute of Education Science and Technology Goias-Cainpuj Rua 75. n°46 - Centro Goiania - GO74055-110 Brazil Electiical Engineering Control and Automation Engineering Program Federal Institute of Education Science and Technology Goias-Cainpuj Rua 75. n°46 - Centro Goiania - GO74055-110 Brazil NeXT - Nucleus of Experimental and Technological Studies Federal Institute of Education Science and Technology Goias-Cainpuj Rua 75. n°46 - Centro Goiania - GO74055-110 Brazil

In 2016. die Brazilian Agency of Electric Energy-launched a public call for research and development projects to reduce barriers to implementing energy efficiency projects and own electricity generation. One objective of this public call was to encourage pilot projects at the Brazilian public higher education institutions. In this context, the Federal Institute of Education. Science, and Technology of Goias has approved the research project, entided "Energy efficiency and mini generation ai the Federal Institute of Education. Science, and Technology - IFG." The project consists of implementing energy efficiency actions and installing photovoltaic distributed generation (mini generation) in twelve of the fourteen IFG campuses. The main objective of this work is to quantify the C0;emission reductions from die project through the methodologies used by the United Nations Framework Climate Change Convention for projects under the Clean Development Mechanism The emission reductions over the project lifetime was estimated at approximately 37,400 iC’O- equivalent, evidencing the environmental benefit to be provided by the referred project. © 2021, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Energy efficiency

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Linear parameter varying control of wind energy conversion systems in partial load 19

Linear parameter varying control of wind energy conversion s...

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19th International Conference on Machine Learning and Cybernetics, ICMLC 2020

作者： Xu, Jing Sun, Qing East China University of Science and Technology Ministry of Education The Key Laboratory of Advanced Control and Optimization for Chemical Process Shanghai200237 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China

ISBN: (纸本)9780738124261

This paper is dealt with the rotor speed tracking problem of variable-speed wind turbine systems operating under the partial load condition. Singular perturbation techniques are used to characterize the two-time-scale property of the wind turbine system, and a linear parameter varying (LPV) model is formed to approximate nonlinear behaviours of a wind turbine system. Based on the slow-fast decomposition method, slow and fast subsystems are constructed: one for the mechanical dynamics and the other for the electrical dynamics. Slow and fast controls are derived, respectively, and then a local state feedback controller, sum of the slow and fast control, is formulated. A design procedure, using the linear parameter varying control to combine local controllers, is proposed to guarantee the robustness of the closed-loop nonlinear wind turbine system. Numerical examples are given to show the validity of the proposed control scheme. © 2020 IEEE.

关键词： Wind turbines

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Stochastic configuration networks with robust supervised least squares regression

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Neurocomputing 2025年 647卷

作者： Chu, Fei Sun, Zihang Wang, Yu Zhang, Yong School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The State Key Laboratory of Process Automation in Mining and Metallurgy/Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100160 China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China

Stochastic Configuration Networks (SCNs) are widely used in regression modeling to fit data distributions due to their fast convergence and powerful learning capabilities. However, in practical industrial applications, labeled outliers in the dataset inevitably appear, which can destroy the internal structure and connection of the data, and not only seriously reduce the prediction accuracy of SCNs, but may also increase the risk of overfitting. To cope with this problem, this paper proposes a new algorithm - Robust Supervised Stochastic Configuration Networks (RSSCNs). In this paper, firstly, the loss function is constructed based on the concave function of the error, which extends the applicability of the robust supervised least squares regression. Second, robust supervised least squares regression is introduced into the SCN algorithm for the first time, which enhances the robustness of the model and reduces the risk of model overfitting. Finally, the inequality constraint of the SCN algorithm is reconstructed according to the loss function, which optimizes the parameter selection and accelerates the convergence of the model. In the experimental section, RSSCNs are applied to a function fitting experiment, several benchmark datasets, and an industrial dataset from a dense medium coal preparation process, and the performance is compared with other algorithms. The experimental results show that for these datasets, RSSCNs perform favorably in terms of accuracy and robustness, validating the effectiveness of the algorithm. © 2025

关键词： Linear regression

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H ∞ Asynchronous control for Discrete-Time Semi-Markov Jump Systems

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IFAC-PapersOnLine 2022年第3期55卷 143-148页

作者： Liqiang Jin Yanyan Yin Yanqing Liu Kok Lay Teo Fei Liu National Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of Automation Jiangnan University Wuxi 214122 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Western Australia 6102 Australia School of Mathematical Sciences Sunway University Kuala Lumpur 47500 Malaysia Tianjin University of Finance and Economics Tianjin China

This paper investigates the problem of H ∞ asynchronous control for discrete-time semi-Markov jump systems. In practice, it is difficult to ensure that controller modes and system modes are updated synchronously. Therefore, a hidden semi-Markov model is introduced to characterize such an asynchronous phenomenon between system modes and controller modes. Meanwhile, the switching signal of the controller is generated by a given conditional probability related to the system mode. Based on the mode-dependent and elapsed-time-dependent Lyapunov function, together with the semi-Markov kernel (SMK) method, sufficient conditions constructed by linear matrix inequalities (LMIs) are derived to ensure the existence of an asynchronous controller such that the resulting system is σ-error mean square stable and satisfies an H ∞ performance index. Finally, a numerical example is given to illustrate the effectiveness and feasibility of the method proposed.

关键词： Asynchronous control semi-Markov jump systems hidden semi-Markov model semi-Markov kernel (SMK) σ-error mean square stable

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