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2021 China Automation Congress, CAC 2021

作者： Huan, Xuwei Niu, Dan Li, Qi Yang, Jun Liu, Guoyao Chen, Xisong Xiao, Xi Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University Nanjing China Department of Aeronautical and Automotive Engineering Loughborough University LoughboroughLE11 3TU United Kingdom Nanjing Sciyon Automation Group Co. Ltd Nanjing China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing China

ISBN: (纸本)9781665426473

This paper proposes a state observer and controller based on the second-order sliding mode scheme for underactuated overhead cranes with load hoisting and lowering. The designed state observer can effectively estimate the uncertainly and unmodeled dynamics of crane systems, and provide appropriate driving force compensations for the sliding mode controller designed by coupling the load swing angle. This guarantees an accurate load positioning and residual swing elimination. Based on the Lyapunov stability criterion, the asymptotic stability of the control system is proved Several simulations are implemented to verify the effectiveness and robustness of the proposed controller for anti-sway and positioning controls. © 2021 IEEE

关键词： Asymptotic stability

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Finite-Time Prescribed Performance control for Dynamic Positioning of Pneumatic Servo system

Finite-Time Prescribed Performance Control for Dynamic Posit...

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IEEE Conference on systems, Process & control (ICSPC)

作者： Mohd Iskandar Putra Azahar Addie Irawan Mohd Syakirin Ramli Robotics Intelligent System & Control System (RISC) Research Group Faculty of Electrical & Electronics Engineering Technology Pekan Pahang Malaysia

ISBN: (数字)9781728188614

ISBN: (纸本)9781728188621

In this paper, a new prescribed performance function (PPF) with a finite time feature is introduced and integrated as a transformation of the output error for the position control on the pneumatic servo system. The error transformation with a new PPF is formulated named as the finite time prescribed performance control (FTPPC) is attached with PID control that is used for servo pneumatic rod-piston positioning. The pneumatic proportional valve with a double-acting cylinder (PPVDC) system model plant is used as a targeted plant for simulation and comparison study with the conventional PID controller. The improved performance and efficiency of the proposed FTPPC with PID approach is simulated and analyzed by using three different types of input trajectory that include the step input, sinusoidal input dynamic ramp input trajectory as well as the addition of payload as external disturbances. The results show that the proposed FTPPC is capable of improving the nominal controller, such as PID in reducing a steady-state error with a fast settling time as well as low overshoot as compared to the traditional PID controller.

关键词： Pneumatic systems Valves Friction Convergence control systems Transient analysis Steady-state

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Cogging Force Identification Based on Self-Adaptive Hybrid Self-Learning TLBO Trained RBF Neural Network for Linear Motors 13

Cogging Force Identification Based on Self-Adaptive Hybrid S...

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13th International Symposium on Linear Drives for Industry Applications, LDIA 2021

作者： Fu, Xuewei Ding, Chenyang Zanchetta, Pericle Yang, Xiaofeng Tang, Mi Liu, Yang State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical University of Pavia Pavia Italy Power Electronics Machine and Control Group University of Nottingham Nottingham United Kingdom Center of Ultra-Precision Optoelectronic Instrument Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9781728172101

The cogging force arising due to the strong attraction forces between the iron core and the permanent magnets, is a common inherent property of the linear motors, which significantly affects the control performance. Therefore, significant research efforts have been devoted to the compensation of the cogging force. In this paper, an identification approach based on the radial basis function neural network (RBFNN) is proposed to obtain an accurate model of the cogging force. A self-adaptive hybrid self-learning teaching-learning-based optimization (SHSLTLBO) method is utilized to train the neural network. Finally, the experimental results confirm the effectiveness and the superiority of the proposed cogging force identification method. © 2021 IEEE

关键词： Radial basis function networks

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Deep neural network based interpolation of sparse samples for time-dense power load forecasting 4

Deep neural network based interpolation of sparse samples fo...

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4th International Conference on Computer Engineering and Application, ICCEA 2023

作者： Li, Bo Zhao, Ruifeng Lu, Jiangang Xin, Kuo Huang, Jinhua Lin, Guanqiang Chen, Jinrong Pang, Xueyue Wu, Shunxin Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou China Electric Power Dispatching and Control Center of China Southern Power Grid Co. Ltd. Guangdong Guangzhou China Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangdong Guangzhou China Huizhou Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Huizhou China Foshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. Guangdong Foshan China China Energy Engineering Group Guangdong Electric Power Design Institute Co. Ltd. Guangdong Guangzhou China School of Electrical Engineering Chongqing University State Key Laboratory of Power Equipment & System Security and New Technology Chongqing China

ISBN: (纸本)9798350347548

Large amount of power load data with high-sampling rate plays a crucial role in the training of time-dense power load forecasting network. However, in many cases, there is a large amount of data with low-sampling rate, which can't directly train a time-dense forecasting network. Therefore, this paper proposes a deep learning based dataset interpolation method, which uses the available high-sampling rate power load datasets to train a dataset interpolation network, then interpolates the low - sampling rate datasets with trained network to obtain high-sampling rate datasets, and finally uses the interpolated datasets for time-dense forecasting network training. The results show that the data obtained based on the method proposed in this paper fits the ground-truth closer than the traditional interpolation, and the results of the network trained based on this dataset are more accurate in the subsequent forecasting tasks. © 2023 IEEE.

关键词： Interpolation

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Fuzzy optimal scheduling of hydrogen-integrated energy systems with uncertainties of renewable generation considering hydrogen equipment under multiple conditions

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Applied Energy 2025年 393卷

作者： Song, Jianzhao Wang, Na Zhang, Zhong Wu, Hao Ding, Yi Pan, Qingze Pan, Xingzuo Shui, Siyuan Chen, Haipeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Gansu Branch of Luneng New Energy Group Co. Ltd. Lanzhou730000 China Lanzhou Power Supply Company State Grid Gansu Electric Power Co. Ltd. Lanzhou730000 China TBEA Shenyang Transformer Group Co. Ltd. Shenyang110144 China Northeast Electric Power University Jilin132012 China

Developing hydrogen-integrated energy systems (HIES) represents a cutting-edge strategy for harnessing renewable energy (RE). However, the inherent unpredictability and variability of RE significantly increase the operational uncertainty of HIES, which leads to severe energy curtailment issues when HIES integrates large-scale RE, necessitating greater operational flexibility in the system. To address the challenges, this paper proposes a fuzzy optimal scheduling approach for HIES that considers the hydrogen equipment under multiple operating conditions. The analysis begins by examining the operational mechanisms of hydrogen equipment. Subsequently, a multi-conditions model is developed for electrolyzers, while a reserve model is established for hydrogen fuel cells. Furthermore, the fuzzy chance constraint (FCC) is employed to quantify the uncertainty of RE generation. An integrated demand response mechanism is implemented, incorporating human thermal comfort and building thermal inertia. Finally, a fuzzy optimization scheduling model for HIES is constructed to minimize the total operating costs. The crisp equivalent of FCC is derived to solve this model, thereby transforming the scheduling model based on fuzzy chance-constrained programming into a solvable mixed-integer programming model. The simulation results indicate that the proposed scheduling method can reduce the overall costs of the HIES by 17.98 % and increase the RE accommodation rate by 19.67 %, validating the effectiveness of the method in enhancing the operational flexibility of the HIES. In addition, this study achieves a balance between economic efficiency and reliability, offering a better economy, lower energy curtailment rates, and faster decision-making times compared to robust optimization, scenario analysis, and other common fuzzy optimization methods. © 2025

关键词： Risk perception

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Pitfalls of guaranteeing asymptotic stability in LPV control of nonlinear systems

arXiv

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arXiv 2020年

作者： Koelewijn, P.J.W. Sales Mazzoccante, G. Tóth, R. Weiland, S. Control System Group Faculty of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands

Recently, a number of counter examples have sur-faced where Linear Parameter-Varying (LPV) control synthesis applied to achieve asymptotic output tracking and disturbance rejection for a nonlinear system, fails to achieve the desired asymptotic tracking and rejection behavior even when the scheduling variations remain in the bounded region considered during design. It has been observed that the controlled system may exhibit an oscillatory motion around the equilibrium point in the presence of a bounded constant input disturbance even if integral action is present. This work aims at investigating how and why the baseline Lyapunov stability notion, currently widely used in the LPV framework, fails to guarantee the desired system behavior. Specifically, it is shown why the quadratic Lyapunov concept is insufficient to always guarantee asymptotic stability under reference tracking and disturbance rejection scenarios, and why an equilibrium independent stabil-ity notion is required for LPV stability analysis and synthesis of controllers. The introduced concepts and the apparent pitfalls are demonstrated via a simulation example. Copyright © 2020, The Authors. All rights reserved.

关键词： Asymptotic stability

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Scheduling Dimension Reduction of LPV Models - A Deep Neural Network Approach

arXiv

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arXiv 2020年

作者： Koelewijn, P.J.W. Tóth, R. Control System Group Faculty of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands

In this paper, the existing Scheduling Dimension Reduction (SDR) methods for Linear Parameter-Varying (LPV) models are reviewed and a Deep Neural Network (DNN) approach is developed that achieves higher model accuracy under scheduling dimension reduction. The proposed DNN method and existing SDR methods are compared on a two-link robotic manipulator, both in terms of model accuracy and performance of controllers synthesized with the reduced models. The methods compared include SDR for state-space models using Principal Component Analysis (PCA), Kernel PCA (KPCA) and Autoencoders (AE). On the robotic manipulator example, the DNN method achieves improved representation of the matrix variations of the original LPV model in terms of the Frobenius norm compared to the current methods. Moreover, when the resulting model is used to accommodate synthesis, improved closed-loop performance is obtained compared to the current methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling

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Enhancing Power system Stability: A Machine Learning Approach with SVM, Random Forest and Deep Learning Utilizing Wide Area Measurements

Enhancing Power System Stability: A Machine Learning Approac...

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IEEE Student Conference on Electric Machines and systems

作者： Chao Song Mengcan Tan Mingzhao Chu Chihan Zhou Guojie Tian Longfei Mu Shandong Future Intelligent Technology Co. Ltd Shandong Electrical Engineering & Equipment Group Co. Ltd Jinan China Fengtai Supply Company Distribution Operation and Maintenance Center State Grid Beijing Electric Power Company Beijing China Changyi Supply Company Marketing Department State Grid Shandong Electric Power Company Changyi China State Grid Kunming Power Supply Company Kunming China Shandong Tianyu Changtong Energy Group Co. Ltd. Qingdao China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

As the scale and complexity of modern power systems continue to expand, the importance of system stability has become increasingly critical. To ensure the stable operation of the power system, this paper proposes a power system stability analysis and optimal control strategy based on the Wide Area Measurement system (WAMS). Initially, a novel stability evaluation method is established using WAMS data and machine learning techniques, enabling effective handling of complex power system data and providing accurate stability assessments. Following this, an optimal control strategy is designed based on the evaluation method, aimed at real-time adjustments of power resource allocation to enhance system stability and responsiveness. Simulation analyses confirm the effectiveness and reliability of the proposed approach, indicating that the optimized control strategy significantly improves the system's performance under disturbances. This paper presents an innovative method for the stable operation of integrated power systems, contributing to the enhancement of power supply quality within the grid and ensuring the safe and efficient operation of the overall power system.

关键词： Time-frequency analysis system performance Optimal control Power system stability system recovery Stability analysis Real-time systems Wide area measurements Voltage control Thermal stability

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CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth

CodeVIO: Visual-Inertial Odometry with Learned Optimizable D...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xingxing Zuo Nathaniel Merrill Wei Li Yong Liu Marc Pollefeys Guoquan Huang ETH Zürich Institute of Cyber-System and Control Zhejiang University Robot Perception and Navigation Group University of Delaware Inceptio Technology Shanghi China Microsoft Mixed Reality and Artificial Intelligence Lab Zürich

In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial odometry (VIO) system, which can provide accurate state estimates and dense depth maps of the immediate surroundings. Leveraging the proposed lightweight Conditional Variational Autoencoder (CVAE) for depth inference and encoding, we provide the network with previously marginalized sparse features from VIO to increase the accuracy of initial depth prediction and generalization capability. The compact representation of dense depth, termed depth code, can be updated jointly with navigation states in a sliding window estimator in order to provide the dense local scene geometry. We additionally propose a novel method to obtain the CVAE’s Jacobian which is shown to be more than an order of magnitude faster than previous works, and we additionally leverage First-Estimate Jacobian (FEJ) to avoid recalculation. As opposed to previous works that rely on completely dense residuals, we propose to only provide sparse measurements to update the depth code and show through careful experimentation that our choice of sparse measurements and FEJs can still significantly improve the estimated depth maps. Our full system also exhibits state-of-the-art pose estimation accuracy, and we show that it can run in real-time with single-thread execution while utilizing GPU acceleration only for the network and code Jacobian.

关键词： Jacobian matrices Geometry Codes Automation Navigation Conferences Pose estimation

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system configurations for shuttle kiln waste heat recovery: comparing and combining a packed-bed thermal storage system and a heat exchanger

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Applied Thermal Engineering 2025年 278卷

作者： Nikolaos Georgousis Jan Diriken Michel Speetjens Camilo Rindt Flemish Institute for Technological Research (VITO) Water and Energy Transitions Unit Analysis and Control of Energy System Operation Group Boeretang 200 2400 Mol Belgium EnergyVille Thor Park 8300 3600 Genk Belgium Eindhoven University of Technology Mechanical Engineering Energy Technology Group Groene Loper 3 5612AE Eindhoven the Netherlands

Shuttle kilns (SKs) are industrial furnace-type equipment used in sanitaryware production, and commonly consume natural gas. The resulting waste heat (WH) is often rejected into the environment. This study compares three distinct system configurations for waste heat recovery to preheat the combustion air, which are: a standalone heat exchanger (HEX), a packed-bed thermal storage (PBTS) system and a combination of both. All three configurations are evaluated based on the amount of the recovered WH per SK cycle, the equipment’s payback period (PBP) and the levelized cost of heat (LCOH). For analyzing the PBTS system, a 1-dimensional numerical model, validated with experimental data from literature, is used. In the first configuration, a HEX can recover up to 8.54 GJ of WH, with a PBP and LCOH of 0.93 years and 0.0170 €/kWh th . In the second configuration, a PBTS system, sized so that it can be fully discharged by the combustion air, can recover around 15 GJ of WH, achieving a PBP and LCOH of 0.17–0.32 years and 0.003–0.006 €/kWh th , respectively. The third configuration aims at maximizing the recovered WH, which reached 15.8 GJ. The accompanying PBP and LCOH were 0.81 years and 0.0148 €/kWh th . Additionally, the reduction in annual CO 2 emissions for the three configurations is up to 16.6 % (HEX), 29.3 % (PBTS) and 30.7–30.8 % (combination). This study highlights that an optimally sized PBTS system can outperform a HEX system, recovering 28 % of the SK’s total heat input and achieving lower PBP and LCOH values.

关键词： Packed-bed thermal storage Sensible heat storage Waste heat recovery for industry Application in a ceramic shuttle kiln Natural gas consumption reduction Thermocline zone velocity

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