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Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

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Advances in Manufacturing 2023年第4期11卷 694-707页

作者： Feng Li Li Jia Ya Gu College of Electrical and Information Engineering Jiangsu University of TechnologyChangzhou213001JiangsuPeople’s Republic of China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai200444People’s Republic of China School of Electrical Engineering and Automation Changshu Institute of TechnologyChangshu215500JiangsuPeople’s Republic of China

In this study,a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is *** Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear ***-signal sources are designed for achieving identification separation of the Hammerstein-Wiener *** correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals,thus the interference of process disturbance is ***,the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals,and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise ***,convergence analysis of the suggested identification scheme is derived using stochastic process *** simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.

关键词： Nonlinear process Parameter identification Hammerstein-Wiener model Neural fuzzy model Multiple signal processing

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SSA-TCN-SVM based Short Wind Power Forecast Oriented to Dual Carbon Target 3

SSA-TCN-SVM based Short Wind Power Forecast Oriented to Dual...

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3rd Power System and Green Energy Conference, PSGEC 2023

作者： Li, Jianfang Jia, Li Peng, Daogang Zhao, Yang College of Mechatronics Shanghai University Department of Automation Shanghai China School of Automation Engineering Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9798350340099

Seeking green and low-carbon development has become a common consensus of the international community, and wind energy is one of the best new energy sources for the development of low-carbon economy. However, wind power has strong randomness and volatility, which will bring new challenges to the green and low-carbon operation of new power systems. Relying on the new power system, accurate power prediction of wind turbines can not only optimize the power supply structure of the power system, but also promote energy conservation, emission reduction and 'low-carbon' development of the power industry. Therefore, under the above background, this paper proposes a short-term wind power prediction model based on Singular Spectrum Analysis-Temporal Convolutional Network-Support Vector Machine (SSA-TCN-SVM). Aiming at the problem that the Temporal Convolutional Network (TCN) prediction model still has complex modes and the Dense Layer cannot set the weights reasonably, a cascade model based on Temporal Convolutional Network-Support Vector Machine (TCN-SVM) is adopted. The kernel function of Support Vector Machine (SVM) is used to map the vectors output by the convolution kernel channel of each time to the high-dimensional kernel space. The optimal weights are obtained by solving the dual problem, and the prediction ability of the single grouping sequence prediction model is improved. By minimizing the prediction error of the grouping sequence, the prediction accuracy of the whole prediction model is improved. Experiments show that SSA-TCN-SVM has better prediction performance. © 2023 IEEE.

关键词： Support vector machines

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Auxiliary modelling error and probability density function based neuro-fuzzy short-term wind power prediction 8

Auxiliary modelling error and probability density function b...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Li, Jianfang Jia, Li Peng, Daogang Hou, Rui Shanghai University College of Mechatronics Department of Automation Shanghai China Shanghai University of Electric Power School of Automation Engineering Shanghai China

ISBN: (纸本)9798350328813

Accurate wind power forecasting not only optimizes the power supply structure of integrated energy system but also promotes the development of a low-carbon power industry. However, modeling and predicting wind power sequence using conventional forecasting methods can be hard due to its high volatility and intermittent nature. Thus this study proposes a hybrid model comprising the subsequent steps to improve the prediction accuracy. Firstly, the wind power sequence is denoised and the primary characteristics of the raw data are found using an ensemble empirical mode decomposition algorithm. Secondly, in order to achieve high precision, this paper propose an auxiliary modelling error and probability density function based Neuro-Fuzzy model (NFM) applied in wind power prediction. In particular, an innovative hybrid parameter identification algorithm is constructed by using auxiliary modelling error and probability density function (PDF) to identify parameters of the proposed NFM. This is auxiliary error converges to the invariant set based on Lyapunov's global convergence, and then converting parameter updating into shape control of PDF for auxiliary modeling error, which follows a target pdf to update model parameters. The experiment demonstrates a considerable improvement in accuracy in comparison to the separate models. © 2023 IEEE.

关键词： Auxiliary modelling error Integrated energy system Lyapunov Neuro-fuzzy model Probability density function Wind power

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automation of Financial Capital Supply Chain Optimization Industry Based on Time Series LSTM Algorithm 16

Automation of Financial Capital Supply Chain Optimization In...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Li, Kangyi Zhou, Yang Ulink College of Shanghai Shanghai China Shanghai University School of Mechatronics Engineering and Automation Shanghai China

ISBN: (纸本)9798350381344

With the development of global economic integration and the increase of economic uncertainty in the future, it is very necessary to predict the future capital of financial supply chain industry in order to ensure that capital optimization is within a controllable range in the current macroeconomic environment and current situation. This paper proposed an automated capital prediction strategy of capital supply chain using time series analysis artificial intelligence methods. First, to analyze the fluctuation and tail risk of financial characteristic, this paper explores the financial characteristic to measure the dynamic VaR from the perspectives of volatility, tail and peak with the Bayesian peaks over threshold (POT) model. Then, in order to make the modeling more refined, the forecast targets are split before the modeling with seasonal Autoregressive Integrated Moving Average (ARIMA) model and Prophet model. Finally, the time series modeling of wavelet Long Short-Term Memory (LSTM) model is carried out by two-part analysis method to get linear separated wavelet and non-linear embedded wavelet parts to predict the strong volatility financial capital. Taking the user capital flow of Yu'e Bao platform, the results prove the feasibility and prediction accuracy of the innovative proposed model. © 2023 IEEE.

关键词： Long short-term memory

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High-gain observer-based fixed-time non-singular terminal sliding mode control for Unmanned surface vehicles with actuator failures

High-gain observer-based fixed-time non-singular terminal sl...

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2023 International Conference on automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023

作者： Zhang, Jiaming Zhou, Weixiang Li, Hengyu Dong, Jiayu Wang, Yueying College of Mechatronics Engineering and Automation Shanghai University Shanghai201900 China College of Information Engineering Shanghai Maritime University Shanghai201306 China

ISBN: (纸本)9781510667662

The adaptive tracking control issue for the unmanned surface vehicle (USV) with actuator failures and disturbances is investigated. More specifically, a fixed-time high-order sliding mode observer is designed to estimate the actuator fault. Furthermore, a non-singular sliding mode controller (NSMC) is designed to solve the singular problem that may occur in the sliding mode function under certain circumstances. And then, by using the Lyapunov stability theory, we prove that the proposed control method can ensure all signals in the closed-loop system are bounded and the USV can track the desired trajectory in a fixed time. At last, the simulation example of the USV operating in the fixed-time tracking strategy demonstrates the effectiveness of the control method. © 2023 SPIE.

关键词： Sliding mode control

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Tire Defect Detection Based on Improved Hypernetwork Semantic Segmentation

Tire Defect Detection Based on Improved Hypernetwork Semanti...

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第43届中国控制会议

作者： Xu Li Chen Peng Xiaoyu Li Penghui Zhang Department of Automation College of Mechatronics Engineering and Automation Shanghai University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Given the complex and diverse textures of tire X-ray images, traditional manual inspections fall short of meeting production needs, making intelligent vision inspection a highly sought-after solution. This paper proposes an improved hypernetwork semantic segmentation network for detecting internal tire defects. In this network, a nested U-Net within the hypernetwork is designed to extract high-level contextual features. Furthermore, the decoder, composed of multiple dynamic blocks,effectively combines low-level and high-level information to enhance segmentation accuracy for defects of various shapes and sizes. Experimental results show that our proposed network achieves effective detection of different tire defects, particularly complex wrinkle defects in the belt layer. The final model achieved a mIoU score of 82.32%, representing a significant improvement of 13.18% compared to U-Net.

关键词： Tire defect Nested U-Net Semantic segmentation Dynamic convolution block

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Continuous Human Learning Optimization Based Fractional Order PID Controller Design for Twin Rotor Aerodynamic System 8th

Continuous Human Learning Optimization Based Fractional Orde...

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Hanif, Bushra Menhas, Muhammad Ilyas Hu, Anyang Wang, Ling Hui, Chen Fei, MinRui AJ&K Mirpur10250 Pakistan Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China College of Automation Engineering Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9789819601875

In this work conventional PID and fractional order PID (FOPID) controllers are designed for Twin Rotor Aerodynamic System (TRAS). TRAS is a nonlinear and multi input multi output (MIMO) system. The aim of the proposed control scheme is to control the pitch and yaw angles of TRAS so that they can move at desired angles. A metaheuristic optimization technique named as continuous human learning optimization algorithm (CHLO) is adopted to design PID and FOPID controllers. PID and FOPID controllers are applied to coupled and decoupled TRAS. The results obtained through simulation revealed that FOPID controller has superior performance than PID controller. Moreover, the response of FOPID and PID controllers is better for decoupled system whereas for coupled system response of designed controller is poor. The results obtained with CHLO-FOPID controller are also compared with other metaheuristic optimization techniques that are used to design FOPID controller for TRAS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Three term control systems

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EMI Noise Reduction with Magnetic Integrated LCL Filter in the Grid-Connected Inverter 49

EMI Noise Reduction with Magnetic Integrated LCL Filter in t...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Liu, Xingyu Liu, Yitao Jiang, Shiqi College of Mechatronics and Control Engineering Shenzhen University Shenzhen China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350331820

This paper presents a magnetic integration scheme of LCL filter with symmetrical structure. This magnetic integration scheme uses EIE core to realize the inductor integration of symmetrical LCL filter, and realizes the decoupling and integration of upper and lower E-cores. The effectiveness of the magnetic integrated symmetrical LCL filter is verified by simulation and experiment. A 600 W single-phase SiC high-frequency inverter was used as the experimental platform to give the output current harmonic analysis results and conducted EMI noise. The symmetrical LCL filter with integrated structure not only has good harmonic suppression effect, but also greatly improves EMI suppression ability. The integrated filter is significantly smaller weight, helping to increase system power density. © 2023 IEEE.

关键词： Noise abatement

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Data-Driven Stochastic-Robust Planning for Resilient Hydrogen-Electricity System with Progressive Hedging Decoupling

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IEEE Transactions on Sustainable Energy 2024年第3期16卷 1545-1561页

作者： Wen, Ziyi Zhang, Xian Wang, Guibin Li, Yiqun Qiu, Jing Wen, Fushuan Harbin Institute of Technology Shenzhen School of Mechanical Engineering and Automation 518055 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Southeast University School of Transportation Nanjing211189 China The University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia Zhejiang University College of Electrical Engineering Hangzhou310027 China

Given the severe damage that typhoons can inflict on power systems, it is of utmost urgency to develop proactive pre-disaster planning to enhance power system resilience and mitigate economic losses. To this end, considering the high efficiency and long-term storage capabilities of hydrogen energy, a resilience-oriented planning model of the hydrogen-electricity integrated energy system (H-EIES) is constructed in this work. In this resilience-oriented planning model, hardening power lines and constructing hydrogen-to-power stations (H2Ps) are jointly planned to reduce load shedding during typhoons. A tri-layer stochastic-robust optimization (SRO) approach is proposed to find the optimal construction strategy under uncertainties, where the first layer determines the planning strategy, the second layer identifies the worst outage contingency, and the third layer ensures the optimal operation. However, the uncertainty of typhoons presents significant challenges to the feasibility of the proposed SRO approach. Therefore, this work leverages the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and spectral clustering method to quantify the typhoon uncertainty, capturing the spatial correlations of 2-dimensional (2D) gridded wind speeds and exploring typical typhoon scenarios. Additionally, a novel nested column-and-constraint generation with progressive hedging algorithm (C&CG-PHA) is elaborately designed to solve the complex tri-layer and multi-scenario coupled optimization problem. In this developed C&CG-PHA, C&CG is employed to decouple the tri-layer planning model into a master problem and a subproblem for iterative solving, and PHA is employed to decouple the multi-scenario embedded master problem into serval scenario-independent problems to improve the solving efficiency. Validating by the real-life typhoon data, numerical experiments are carried out and indicate the economic efficiency and robustness of the proposed SRO approach, where th

关键词： Stochastic systems

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Multi-Channel Knowledge Distillation via Efficient Feature Fusion Module for Anomaly Segmentation

Multi-Channel Knowledge Distillation via Efficient Feature F...

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2023 China automation Congress, CAC 2023

作者： Zhou, You Huang, Zihao Zeng, Deyu Wu, Zongze School of Automation Guangdong University of Technology Guangzhou China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350303759

Unsupervised image anomaly detection and segmentation algorithms hold significant importance in enhancing product quality in real-world industrial manufacturing. Anomaly detection methods based on the knowledge distillation framework perform detection by characterizing differences between anomalous samples. However, knowledge distillation-based anomaly detection methods often employ similar or identical architectures for constructing both teacher and student models. This practice has led to the issue of vanishing anomaly activation values, resulting in detection failures. Hence, the proposed Multi-Channel Knowledge Distillation model (MCKD) consists of a pair of student networks proficient at effectively capturing defect-related information. One branch of the student network enhances its diverse representation of anomalies by restoring the input's multi-scale representations. The other branch enriches the model's capacity to represent detailed defect information by extracting features from different scales. Additionally, our design of the Residual Features Aggregation Module (RFAM) ensures better retention of normal pattern information within the student network while disregarding information from abnormal patterns, resulting in enhanced modeling of positive samples. The proposed framework achieves state-of-the-art (SOTA) on the well-known MVTec AD dataset, with an area under the receiver operating characteristic curve of 97.1% for anomaly detection across all 15 categories and an area under the anomaly segmentation of 98.14%. © 2023 IEEE.

关键词： Distillation

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