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arXiv 2022年

作者： Zhu, Dafeng Yang, Bo Ma, Chengbin Wang, Zhaojian Zhu, Shanying Ma, Kai Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users' willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid-ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically. © 2022, CC BY-NC-ND.

关键词： Energy management

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Redundancy reduction penalty term of loss function in deep neural network

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Knowledge-Based Systems 2025年

作者： Xueheng Hu Shuhuan Wen H.K. Lam Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Qinhuangdao 066004 Hebei Province China The Department of Engineering King’s College London London WC2R 2LS United Kingdom

In this paper, we propose a novel regularization algorithm that is introduced as a penalty term to the loss function. Differing from conventional L1 and L2 regularization methods, our approach does not aim to diminish the weights of individual neurons or enforce sparsity by driving certain neurons to zero. Instead, it functions by increasing the differences between neurons and enhancing the diversity of neurons within each layer. Our method incorporates ensemble learning techniques by treating the layer weight matrix as a collective learning model, where each neuron serving as a weak learner within the layer. The proposed algorithm improves the performance of DCNN by simultaneously considering the distance between multiple filters in the same layer. This algorithm reduces the redundancy of the parameter layer filters in DCNN and enhances its robustness. The penalty term proposed by our algorithm dynamically adjusts its value in a cyclical manner, compelling the neural network to navigate away from its current gradient state. In the parameter space, different weights correspond to different locations. The proposed algorithm quantifies the distance between neurons and iteratively increases the distance between neurons during thereby encouraging greater diversity within the network. Experimental evaluations demonstrate the effectiveness of our algorithm in enhancing neural network performance without requiring adjustments to other hyper-parameters.

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An improved particle swarm optimization particle filter algorithm based on harmony search

An improved particle swarm optimization particle filter algo...

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第三十九届中国控制会议

作者： Zhen-dong Liu Yi-ming Fang Le Liu Xiao-dong Zhao Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University

In light of the problems that particle filter based on particle swarm optimization（PSO-PF） algorithm is easy to fall into the local optimum in the state estimation of complex nonlinear systems, an improved particle swarm optimization particle filter algorithm based on harmony search（HSPSO-PF） is proposed. The optimal value of each individual in the particle swarm algorithm is equivalent to a variable in the harmony memory（HM） in the harmony search algorithm. On the one hand, the idea of generating solutions in harmony search algorithm is used to update the matrix that is composed of particles’ optimal solutions in the iteration of PSO. And the harmony memory consideration rate is adjusted to ensure that the source of solution generation is not limited to the HM, so that the PSO can search for the global optimal solution. On the other hand, the pitch adjustment rate and the fine-tuning bandwidth are adjusted to improve the local search ability of the PSO. Simulation results under Gaussian and non-Gaussian show that HSPSO-PF can effectively improve the estimation accuracy of the particle filtering algorithm, and has extensive adaptability.

关键词： Particle filter Harmony search Particle swarm optimization

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A Novel Fuzzy Model Identification Approach Based on FCM and Gaussian Membership Function

A Novel Fuzzy Model Identification Approach Based on FCM and...

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Chinese control Conference (CCC)

作者： Yaxue Ren Jinfeng Lv Fucai Liu Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering (FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares (RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.

关键词： Mathematical model Clustering algorithms Matrices Input variables Marine vehicles Partitioning algorithms MISO communication

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Expression recognition algorithm based on local and global feature reconstruction residuals

Expression recognition algorithm based on local and global f...

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International Conference on Information Science and control engineering (ICISCE)

作者： Yaqian Li Ya Lei Chao Wu Haibin Li Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China

In this paper, an algorithm is proposed to improve expression recognition by combining reconstructed residuals of local and global information. Firstly, positional relation is introduced into local binary features encoding, and a non-negative matrix decomposition method with sparse correlation constraints is employed to perform reducing of dimension. Then, the features are encoded by bag-of-words model to form local features. Secondly, a discrete wavelet transform is used to emphasis on expression information and extract expression-enhanced global features. Thirdly, collaboration Represents is used to calculate local partial reconstruction residuals and is combined with global reconstruction residuals to construct the final residual to be classified at the decision level. Experiments on Jaffe, TEFID and CK+ data sets prove the effectiveness of the proposed algorithm.

关键词： Information science Correlation control engineering Transforms Feature extraction Encoding Classification algorithms

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Integral Sliding Mode control for PMSM with Uncertainties and Disturbances via Nonlinear Disturbance Observer

Integral Sliding Mode Control for PMSM with Uncertainties an...

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Chinese control Conference (CCC)

作者： Jianxiong Li Hongmei Ren Songhua Wang Yiming Fang Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China The Key Lab of Industrial Computer Control Engineering of Yanshan University Qinhuangdao China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, a nonlinear disturbance observer-based (NDOB) integral sliding mode control (SMC) algorithm is proposed for speed control of permanent magnet synchronous motor (PMSM) in the presence of uncertain parameters and external time-varying load toque. Firstly, three nonlinear disturbance observers (NDOs) are constructed to estimate the lumped disturbances comprising of uncertain parameters and/or disturbances in speed loop and two current loops, respectively. Secondly, an integral sliding mode controller is developed based on the NDOs. It can be proved that the resulting closed loop system is asymptotically stable and the speed tracking error converges to zero asymptotically in theory. Finally, experiment results are given to validate the proposed control method.

关键词： Torque Disturbance observers Sliding mode control Uncertainty Velocity control Mathematical model Upper bound

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Solving non-cyclic dynamic multi-objective optimization problems via GRU prediction and multi-information hybrid exploration

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Journal of Supercomputing 2025年第8期81卷

作者： Sun, Hao Bai, Xiaochuang Wang, Cong Yang, He Hu, Ziyu School of Electrical and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Qinhuangdao066004 China Electrical automation department Tianjin Research Institute of Electric Science Co. Ltd. Tianjin China

In non-cyclic dynamic multi-objective optimization problems, the non-cyclic nature of environmental changes may cause the Pareto optimal front (PF) to be different from historical times. In addition, changes may also occur on the Pareto optimal solutions set (PS). However, predictions based solely on a single space show lower accuracy due to insufficient information. Therefore, solving non-cyclic dynamic multi-objective optimization problems via gate recurrent unit (GRU) prediction and multi-information hybrid exploration (GPMHE) is designed to overcome the above challenges. In the objective space, the GRU-based prediction strategy (GP) captures patterns of population change, predicts its distribution in new environments, and subsequently maps the results to the decision space;The multi-information hybrid exploration strategy (MHE) takes the feature individual as the representative individual, adaptively guides the population evolution direction in the decision space, thereby enhancing the algorithm’s adaptability. Compared with five advanced algorithms on the non-cyclic dynamic benchmark test set (NCD), the proposed algorithm GPMHE has shown robust adaptation to dynamic environmental changes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Multiobjective optimization

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A Novel Fuzzy Model Identification Approach Based on FCM and Gaussian Membership Function

A Novel Fuzzy Model Identification Approach Based on FCM and...

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第三十九届中国控制会议

作者： Yaxue Ren Jinfeng Lv Fucai Liu Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Normal University of Science and Technology

To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering(FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares(RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.

关键词： T-S fuzzy modeling Fuzzy identification Fuzzy c-means Gaussian function

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Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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A PD- Type iterative learning algorithm for networked control systems with missing data 2019

A PD- Type iterative learning algorithm for networked contro...

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2019 International Conference on Artificial Intelligence and computer Science, AICS 2019

作者： Cheng, Xiaoyan Dong, Yahui Wang, Hongbin Wang, Qinzhao Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Weapon and Control Department Army Academy of Armored Forces Beijing100072 China

ISBN: (纸本)9781450371506

In this paper, a PD-type iterative learning control method is proposed for a class of nonlinear discrete networked control systems with measurement signal and control signal data dropouts. Proportional and differential items of the error signal are employed in this method to modify the current control signal, which takes full advantage of the historical error signals. The data dropout is described as a stochastic and independent Bernoulli process with a given probability. In addition, the zero-order holding method is introduced at the data receivers of the controller and actuator. Finally, the stability analysis and simulation results are performed to verify the convergence and effectiveness of the proposed method. © 2019 Association for Computing Machinery.

关键词： Networked control systems

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